Darijo Raca, Jason J. Quinlan, A. Zahran, C. Sreenan
In this paper, we present a 4G trace dataset composed of client-side cellular key performance indicators (KPIs) collected from two major Irish mobile operators, across different mobility patterns (static, pedestrian, car, bus and train). The 4G trace dataset contains 135 traces, with an average duration of fifteen minutes per trace, with viewable throughput ranging from 0 to 173 Mbit/s at a granularity of one sample per second. Our traces are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. This tool enables capturing various channel related KPIs, context-related metrics, downlink and uplink throughput, and also cell-related information. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 4G networks. To supplement our real-time 4G production network dataset, we also provide a synthetic dataset generated from a large-scale 4G ns-3 simulation that includes one hundred users randomly scattered across a seven-cell cluster. The purpose of this dataset is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the eNodeB environment and scheduling principle, to end user. In addition to this dataset, we also provide the code and context information to allow other researchers to generate their own synthetic datasets.
{"title":"Beyond throughput: a 4G LTE dataset with channel and context metrics","authors":"Darijo Raca, Jason J. Quinlan, A. Zahran, C. Sreenan","doi":"10.1145/3204949.3208123","DOIUrl":"https://doi.org/10.1145/3204949.3208123","url":null,"abstract":"In this paper, we present a 4G trace dataset composed of client-side cellular key performance indicators (KPIs) collected from two major Irish mobile operators, across different mobility patterns (static, pedestrian, car, bus and train). The 4G trace dataset contains 135 traces, with an average duration of fifteen minutes per trace, with viewable throughput ranging from 0 to 173 Mbit/s at a granularity of one sample per second. Our traces are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. This tool enables capturing various channel related KPIs, context-related metrics, downlink and uplink throughput, and also cell-related information. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 4G networks. To supplement our real-time 4G production network dataset, we also provide a synthetic dataset generated from a large-scale 4G ns-3 simulation that includes one hundred users randomly scattered across a seven-cell cluster. The purpose of this dataset is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the eNodeB environment and scheduling principle, to end user. In addition to this dataset, we also provide the code and context information to allow other researchers to generate their own synthetic datasets.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130261067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yashar Deldjoo, M. Constantin, B. Ionescu, M. Schedl, P. Cremonesi
In this paper we propose a new dataset, i.e., the MMTF-14K multi-faceted dataset. It is primarily designed for the evaluation of video-based recommender systems, but it also supports the exploration of other multimedia tasks such as popularity prediction, genre classification and auto-tagging (aka tag prediction). The data consists of 13,623 Hollywood-type movie trailers, ranked by 138,492 users, generating a total of almost 12.5 million ratings. To address a broader community, metadata, audio and visual descriptors are also pre-computed and provided along with several baseline benchmarking results for uni-modal and multi-modal recommendation systems. This creates a rich collection of data for benchmarking results and which supports future development of this field.
{"title":"MMTF-14K: a multifaceted movie trailer feature dataset for recommendation and retrieval","authors":"Yashar Deldjoo, M. Constantin, B. Ionescu, M. Schedl, P. Cremonesi","doi":"10.1145/3204949.3208141","DOIUrl":"https://doi.org/10.1145/3204949.3208141","url":null,"abstract":"In this paper we propose a new dataset, i.e., the MMTF-14K multi-faceted dataset. It is primarily designed for the evaluation of video-based recommender systems, but it also supports the exploration of other multimedia tasks such as popularity prediction, genre classification and auto-tagging (aka tag prediction). The data consists of 13,623 Hollywood-type movie trailers, ranked by 138,492 users, generating a total of almost 12.5 million ratings. To address a broader community, metadata, audio and visual descriptors are also pre-computed and provided along with several baseline benchmarking results for uni-modal and multi-modal recommendation systems. This creates a rich collection of data for benchmarking results and which supports future development of this field.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122211387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tarun Mangla, E. Zegura, M. Ammar, Emir Halepovic, Kyung-Wook Hwang, R. Jana, M. Platania
Video streaming traffic is rapidly growing in mobile networks. Mobile Network Operators (MNOs) are expected to keep up with this growing demand, while maintaining a high video Quality of Experience (QoE). This makes it critical for MNOs to have a solid understanding of users' video QoE with a goal to help with network planning, provisioning and traffic management. However, designing a system to measure video QoE has several challenges: i) large scale of video traffic data and diversity of video streaming services, ii) cross-layer constraints due to complex cellular network architecture, and iii) extracting QoE metrics from network traffic. In this paper, we present VideoNOC, a prototype of a flexible and scalable platform to infer objective video QoE metrics (e.g., bitrate, rebuffering) for MNOs. We describe the design and architecture of VideoNOC, and outline the methodology to generate a novel data source for fine-grained video QoE monitoring. We then demonstrate some of the use cases of such a monitoring system. VideoNOC reveals video demand across the entire network, provides valuable insights on a number of design choices by content providers (e.g., OS-dependent performance, video player parameters like buffer size, range of encoding bitrates, etc.) and helps analyze the impact of network conditions on video QoE (e.g., mobility and high demand).
{"title":"VideoNOC","authors":"Tarun Mangla, E. Zegura, M. Ammar, Emir Halepovic, Kyung-Wook Hwang, R. Jana, M. Platania","doi":"10.1145/3204949.3204956","DOIUrl":"https://doi.org/10.1145/3204949.3204956","url":null,"abstract":"Video streaming traffic is rapidly growing in mobile networks. Mobile Network Operators (MNOs) are expected to keep up with this growing demand, while maintaining a high video Quality of Experience (QoE). This makes it critical for MNOs to have a solid understanding of users' video QoE with a goal to help with network planning, provisioning and traffic management. However, designing a system to measure video QoE has several challenges: i) large scale of video traffic data and diversity of video streaming services, ii) cross-layer constraints due to complex cellular network architecture, and iii) extracting QoE metrics from network traffic. In this paper, we present VideoNOC, a prototype of a flexible and scalable platform to infer objective video QoE metrics (e.g., bitrate, rebuffering) for MNOs. We describe the design and architecture of VideoNOC, and outline the methodology to generate a novel data source for fine-grained video QoE monitoring. We then demonstrate some of the use cases of such a monitoring system. VideoNOC reveals video demand across the entire network, provides valuable insights on a number of design choices by content providers (e.g., OS-dependent performance, video player parameters like buffer size, range of encoding bitrates, etc.) and helps analyze the impact of network conditions on video QoE (e.g., mobility and high demand).","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114263814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hyunwook Kim, JinWook Yang, Minsu Choi, Junsuk Lee, Sangpil Yoon, Younghwan Kim, WooChool Park
To increase the sense of immersion of 360VR images, we have proposed and implemented foveated rendering technology through precision region-of-interest detection using eye-tracking-based head-mounted display equipment for a high-efficiency video coding tiled video-based image-decoding and -rendering method. Our method can provide a high rendering speed and high-quality textures.
{"title":"Eye tracking based foveated rendering for 360 VR tiled video","authors":"Hyunwook Kim, JinWook Yang, Minsu Choi, Junsuk Lee, Sangpil Yoon, Younghwan Kim, WooChool Park","doi":"10.1145/3204949.3208111","DOIUrl":"https://doi.org/10.1145/3204949.3208111","url":null,"abstract":"To increase the sense of immersion of 360VR images, we have proposed and implemented foveated rendering technology through precision region-of-interest detection using eye-tracking-based head-mounted display equipment for a high-efficiency video coding tiled video-based image-decoding and -rendering method. Our method can provide a high rendering speed and high-quality textures.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131852659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hyunwook Kim, JinWook Yang, Minsu Choi, Junsuk Lee, Sangpil Yoon, Yonghwa Kim, WooChool Park
This paper shows how a system was designed and implemented for the live tiled streaming of 360° Virtual Reality(VR) eSports based on HEVC. A virtual camera technique was designed and implemented based on the Unity and Unreal engines as a means of capturing 360° videos in a virtual space, and a MPEG-DASH SRD(Spatial Relation Description)[1-2] Based dashing and streaming server was configured to build a live HEVC encoding system and for real-time internet streaming. Finally, an integrated 360° VR client was configured for use as a multi-platform. Each technological element was designed to service the client through a 360° VR 3D stereo method, and the MPEG-DASH SRD was expanded such that high-quality game videos can be played at lower bandwidths.
{"title":"Immersive 360° VR tiled streaming system for esports service","authors":"Hyunwook Kim, JinWook Yang, Minsu Choi, Junsuk Lee, Sangpil Yoon, Yonghwa Kim, WooChool Park","doi":"10.1145/3204949.3209619","DOIUrl":"https://doi.org/10.1145/3204949.3209619","url":null,"abstract":"This paper shows how a system was designed and implemented for the live tiled streaming of 360° Virtual Reality(VR) eSports based on HEVC. A virtual camera technique was designed and implemented based on the Unity and Unreal engines as a means of capturing 360° videos in a virtual space, and a MPEG-DASH SRD(Spatial Relation Description)[1-2] Based dashing and streaming server was configured to build a live HEVC encoding system and for real-time internet streaming. Finally, an integrated 360° VR client was configured for use as a multi-platform. Each technological element was designed to service the client through a 360° VR 3D stereo method, and the MPEG-DASH SRD was expanded such that high-quality game videos can be played at lower bandwidths.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"39 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131919264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andreas Leibetseder, Stefan Petscharnig, Manfred Jürgen Primus, Sabrina Kletz, Bernd Münzer, Klaus Schoeffmann, J. Keckstein
Modern imaging technology enables medical practitioners to perform minimally invasive surgery (MIS), i.e. a variety of medical interventions inflicting minimal trauma upon patients, hence, greatly improving their recoveries. Not only patients but also surgeons can benefit from this technology, as recorded media can be utilized for speeding-up tedious and time-consuming tasks such as treatment planning or case documentation. In order to improve the predominantly manually conducted process of analyzing said media, with this work we publish four datasets extracted from gynecologic, laparoscopic interventions with the intend on encouraging research in the field of post-surgical automatic media analysis. These datasets are designed with the following use cases in mind: medical image retrieval based on a query image, detection of instrument counts, surgical actions and anatomical structures, as well as distinguishing on which anatomical structure a certain action is performed. Furthermore, we provide suggestions for evaluation metrics and first baseline experiments.
{"title":"Lapgyn4","authors":"Andreas Leibetseder, Stefan Petscharnig, Manfred Jürgen Primus, Sabrina Kletz, Bernd Münzer, Klaus Schoeffmann, J. Keckstein","doi":"10.1145/3204949.3208127","DOIUrl":"https://doi.org/10.1145/3204949.3208127","url":null,"abstract":"Modern imaging technology enables medical practitioners to perform minimally invasive surgery (MIS), i.e. a variety of medical interventions inflicting minimal trauma upon patients, hence, greatly improving their recoveries. Not only patients but also surgeons can benefit from this technology, as recorded media can be utilized for speeding-up tedious and time-consuming tasks such as treatment planning or case documentation. In order to improve the predominantly manually conducted process of analyzing said media, with this work we publish four datasets extracted from gynecologic, laparoscopic interventions with the intend on encouraging research in the field of post-surgical automatic media analysis. These datasets are designed with the following use cases in mind: medical image retrieval based on a query image, detection of instrument counts, surgical actions and anatomical structures, as well as distinguishing on which anatomical structure a certain action is performed. Furthermore, we provide suggestions for evaluation metrics and first baseline experiments.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125315378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christian Koch, Johannes Pfannmüller, Amr Rizk, D. Hausheer, R. Steinmetz
Content delivery networks (CDNs) carry more than half of the video content in today's Internet. By placing content in caches close to the users, CDNs help increasing the Quality of Experience, e.g., by decreasing the delay until a video playback starts. Existing works on CDN cache performance focus mostly on distinct caching metrics, such as hit rate, given an abstract workload model. Moreover, the nature of the geographical distribution and connection of caches is often oversimplified. In this work, we investigate the performance of cache hierarchies while taking into account the presence of a mixed content workload comprising multiple categories, e.g., news, comedy, and music. We consider the performance of existing caching strategies in terms of cache hit rate and deterioration costs in terms of write operations. Further, we contribute a design and an evaluation of a content category-aware caching strategy, which has the benefit of being sensitive to changing category-specific content popularity. We evaluate our caching strategy, denoted as ACDC (Adaptive Content-Aware Designed Cache), using multiple caching hierarchy models, different cache sizes, and a real world trace covering one week of YouTube requests observed in a large European mobile ISP network. We demonstrate that ACDC increases the cache hit rate for certain hierarchies up to 18.39% and decreases transmission latency up to 12%. Additionally, a decrease in disk write operations up to 55% is observed.
{"title":"Category-aware hierarchical caching for video-on-demand content on youtube","authors":"Christian Koch, Johannes Pfannmüller, Amr Rizk, D. Hausheer, R. Steinmetz","doi":"10.1145/3204949.3204963","DOIUrl":"https://doi.org/10.1145/3204949.3204963","url":null,"abstract":"Content delivery networks (CDNs) carry more than half of the video content in today's Internet. By placing content in caches close to the users, CDNs help increasing the Quality of Experience, e.g., by decreasing the delay until a video playback starts. Existing works on CDN cache performance focus mostly on distinct caching metrics, such as hit rate, given an abstract workload model. Moreover, the nature of the geographical distribution and connection of caches is often oversimplified. In this work, we investigate the performance of cache hierarchies while taking into account the presence of a mixed content workload comprising multiple categories, e.g., news, comedy, and music. We consider the performance of existing caching strategies in terms of cache hit rate and deterioration costs in terms of write operations. Further, we contribute a design and an evaluation of a content category-aware caching strategy, which has the benefit of being sensitive to changing category-specific content popularity. We evaluate our caching strategy, denoted as ACDC (Adaptive Content-Aware Designed Cache), using multiple caching hierarchy models, different cache sizes, and a real world trace covering one week of YouTube requests observed in a large European mobile ISP network. We demonstrate that ACDC increases the cache hit rate for certain hierarchies up to 18.39% and decreases transmission latency up to 12%. Additionally, a decrease in disk write operations up to 55% is observed.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"1540 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128063234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mobility tracking of IoT devices in smart city infrastructures such as smart buildings, hospitals, shopping centers, warehouses, smart streets, and outdoor spaces has many applications. Since Bluetooth Low Energy (BLE) is available in almost every IoT device in the market nowadays, a key to localizing and tracking IoT devices is to develop an accurate ranging technique for BLE-enabled IoT devices. This is, however, a challenging feat as billions of these devices are already in use, and for pragmatic reasons, we cannot propose to modify the IoT device (a BLE peripheral) itself. Furthermore, unlike WiFi ranging - where the channel state information (CSI) is readily available and the bandwidth can be increased by stitching 2.4GHz and 5GHz bands together to achieve a high-precision ranging, an unmodified BLE peripheral provides us with only the RSSI information over a very limited bandwidth. Accurately ranging a BLE device is therefore far more challenging than other wireless standards. In this paper, we exploit characteristics of BLE protocol (e.g. frequency hopping and empty control packet transmissions) and propose a technique to directly estimate the range of a BLE peripheral from a BLE access point by multipath profiling. We discuss the theoretical foundation and conduct experiments to show that the technique achieves a 2.44m absolute range estimation error on average.
{"title":"Rethinking ranging of unmodified BLE peripherals in smart city infrastructure","authors":"Bashima Islam, M. Uddin, S. Mukherjee, S. Nirjon","doi":"10.1145/3204949.3204950","DOIUrl":"https://doi.org/10.1145/3204949.3204950","url":null,"abstract":"Mobility tracking of IoT devices in smart city infrastructures such as smart buildings, hospitals, shopping centers, warehouses, smart streets, and outdoor spaces has many applications. Since Bluetooth Low Energy (BLE) is available in almost every IoT device in the market nowadays, a key to localizing and tracking IoT devices is to develop an accurate ranging technique for BLE-enabled IoT devices. This is, however, a challenging feat as billions of these devices are already in use, and for pragmatic reasons, we cannot propose to modify the IoT device (a BLE peripheral) itself. Furthermore, unlike WiFi ranging - where the channel state information (CSI) is readily available and the bandwidth can be increased by stitching 2.4GHz and 5GHz bands together to achieve a high-precision ranging, an unmodified BLE peripheral provides us with only the RSSI information over a very limited bandwidth. Accurately ranging a BLE device is therefore far more challenging than other wireless standards. In this paper, we exploit characteristics of BLE protocol (e.g. frequency hopping and empty control packet transmissions) and propose a technique to directly estimate the range of a BLE peripheral from a BLE access point by multipath profiling. We discuss the theoretical foundation and conduct experiments to show that the technique achieves a 2.44m absolute range estimation error on average.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130562526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jian He, M. Qureshi, L. Qiu, Jin Li, Feng Li, Lei Han
While the increasing scales of the recent WSN deployments keep pushing a higher demand on the network throughput, the 16 orthogonal channels of the ZigBee radios are intensively explored to improve the parallelism of the transmissions. However, the interferences generated by other ISM band wireless devices (e.g., WiFi) have severely limited the usable channels for WSNs. Such a situation raises a need for a spectrum utilizing method more efficient than the conventional multi-channel access. To this end, we propose to shift the paradigm from discrete channel allocation to continuous frequency allocation in this paper. Motivated by our experiments showing the flexible and efficient use of spectrum through continuously tuning channel center frequencies with respect to link distances, we present FAVOR (Frequency Allocation for Versatile Occupancy of spectRum) to allocate proper center frequencies in a continuous spectrum (hence potentially overlapped channels, rather than discrete orthogonal channels) to nodes or links. To find an optimal frequency allocation, FAVOR creatively combines location and frequency into one space and thus transforms the frequency allocation problem into a spatial tessellation problem. This allows FAVOR to innovatively extend a spatial tessellation technique for the purpose of frequency allocation. We implement FAVOR in MicaZ platforms, and our extensive experiments with different network settings strongly demonstrate the superiority of FAVOR over existing approaches.
{"title":"Favor","authors":"Jian He, M. Qureshi, L. Qiu, Jin Li, Feng Li, Lei Han","doi":"10.1145/2491288.2491292","DOIUrl":"https://doi.org/10.1145/2491288.2491292","url":null,"abstract":"While the increasing scales of the recent WSN deployments keep pushing a higher demand on the network throughput, the 16 orthogonal channels of the ZigBee radios are intensively explored to improve the parallelism of the transmissions. However, the interferences generated by other ISM band wireless devices (e.g., WiFi) have severely limited the usable channels for WSNs. Such a situation raises a need for a spectrum utilizing method more efficient than the conventional multi-channel access. To this end, we propose to shift the paradigm from discrete channel allocation to continuous frequency allocation in this paper. Motivated by our experiments showing the flexible and efficient use of spectrum through continuously tuning channel center frequencies with respect to link distances, we present FAVOR (Frequency Allocation for Versatile Occupancy of spectRum) to allocate proper center frequencies in a continuous spectrum (hence potentially overlapped channels, rather than discrete orthogonal channels) to nodes or links. To find an optimal frequency allocation, FAVOR creatively combines location and frequency into one space and thus transforms the frequency allocation problem into a spatial tessellation problem. This allows FAVOR to innovatively extend a spatial tessellation technique for the purpose of frequency allocation. We implement FAVOR in MicaZ platforms, and our extensive experiments with different network settings strongly demonstrate the superiority of FAVOR over existing approaches.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121987014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thierry Malon, G. Roman-Jimenez, Patrice Guyot, S. Chambon, V. Charvillat, Alain Crouzil, A. Péninou, J. Pinquier, F. Sèdes, Christine Sénac
In surveillance applications, humans and vehicles are the most important common elements studied. In consequence, detecting and matching a person or a car that appears on several videos is a key problem. Many algorithms have been introduced and nowadays, a major relative problem is to evaluate precisely and to compare these algorithms, in reference to a common ground-truth. In this paper, our goal is to introduce a new dataset for evaluating multi-view based methods. This dataset aims at paving the way for multidisciplinary approaches and applications such as 4D-scene reconstruction, object identification/tracking, audio event detection and multi-source meta-data modeling and querying. Consequently, we provide two sets of 25 synchronized videos with audio tracks, all depicting the same scene from multiple viewpoints, each set of videos following a detailed scenario consisting in comings and goings of people and cars. Every video was annotated by regularly drawing bounding boxes on every moving object with a flag indicating whether the object is fully visible or occluded, specifying its category (human or vehicle), providing visual details (for example clothes types or colors), and timestamps of its apparitions and disappearances. Audio events are also annotated by a category and timestamps.
{"title":"Toulouse campus surveillance dataset: scenarios, soundtracks, synchronized videos with overlapping and disjoint views","authors":"Thierry Malon, G. Roman-Jimenez, Patrice Guyot, S. Chambon, V. Charvillat, Alain Crouzil, A. Péninou, J. Pinquier, F. Sèdes, Christine Sénac","doi":"10.1145/3204949.3208133","DOIUrl":"https://doi.org/10.1145/3204949.3208133","url":null,"abstract":"In surveillance applications, humans and vehicles are the most important common elements studied. In consequence, detecting and matching a person or a car that appears on several videos is a key problem. Many algorithms have been introduced and nowadays, a major relative problem is to evaluate precisely and to compare these algorithms, in reference to a common ground-truth. In this paper, our goal is to introduce a new dataset for evaluating multi-view based methods. This dataset aims at paving the way for multidisciplinary approaches and applications such as 4D-scene reconstruction, object identification/tracking, audio event detection and multi-source meta-data modeling and querying. Consequently, we provide two sets of 25 synchronized videos with audio tracks, all depicting the same scene from multiple viewpoints, each set of videos following a detailed scenario consisting in comings and goings of people and cars. Every video was annotated by regularly drawing bounding boxes on every moving object with a flag indicating whether the object is fully visible or occluded, specifying its category (human or vehicle), providing visual details (for example clothes types or colors), and timestamps of its apparitions and disappearances. Audio events are also annotated by a category and timestamps.","PeriodicalId":141196,"journal":{"name":"Proceedings of the 9th ACM Multimedia Systems Conference","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122486366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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