Identification of pedestrian attributes based on video sequence

Jia Xu, Hongbo Yang
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引用次数: 3

Abstract

Analysis of pedestrian attributes in surveillance scenes is a challenging task for computer vision due to the influence of illumination and occlusion. Most existing algorithms focus on using static images to estimate results. However, these detectors often fail to appliance in videos. In this paper, We explore the GoogLeNet network that join Short-Term Memory (LSTM) to identify pedestrian attributes in continuous video sequences. Instead of dealing with a single frame, the network is used to predict the order images in run-on time. Extensive experiments are performed on video data in different monitoring conditions such as subway entrance, market and intersection. Results show that the method has achieved competitive performance on attribute recognition.
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基于视频序列的行人属性识别
由于光照和遮挡的影响,对监控场景中的行人属性进行分析是计算机视觉的一个难点。现有的算法大多集中在使用静态图像来估计结果。然而,这些检测器往往不能应用在视频中。在本文中,我们探索了加入短期记忆(LSTM)的GoogLeNet网络来识别连续视频序列中的行人属性。该网络不再处理单帧图像,而是在运行时预测图像的顺序。对地铁入口、市场、十字路口等不同监控条件下的视频数据进行了大量实验。结果表明,该方法在属性识别方面取得了较好的效果。
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