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首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >Fast multi-object tracking using convolutional neural networks with tracklets updating
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Fast multi-object tracking using convolutional neural networks with tracklets updating

机译：使用带小波更新的卷积神经网络进行快速多目标跟踪

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摘要

Many multi-object tracking methods have been developed to solve the computer vision problem which has been attracting significant attentions. In this paper, a novel convolutional neural networks with frame-pair input method for multi-object tracking is presented. It is found that our object tracking methods trained using two successive frames tend to predict the centers of searching windows as the locations of tracked targets. CNN features and color histogram features are extracted as appearance features to measure similarities between objects which used for Tracklets. Kalman Filter and Hungarian algorithm are used to create tracklets association which indicates the location of tracked targets. Specifically, we construct a novel sampling strategy for off-line training. Experiments on the popular challenging datasets show that the proposed tracking system performs on par with recently developed generic multi-object tracking methods, but with much less memory. In addition, our tracking system can run in a speed of over 80 (30) fps with a GPU (CPU), much faster than most deep neural networks based trackers. We found that simply improving detection performance can lead to much better multiple object tracking results.
机译：为了解决计算机视觉问题，已经开发了许多多目标跟踪方法，引起了广泛的关注。本文提出了一种新颖的带帧对输入法的卷积神经网络用于多目标跟踪。发现我们的使用两个连续帧训练的对象跟踪方法倾向于将搜索窗口的中心预测为被跟踪目标的位置。提取CNN特征和颜色直方图特征作为外观特征，以测量用于Tracklet的对象之间的相似性。卡尔曼滤波器和匈牙利算法用于创建跟踪目标的关联，从而指示跟踪目标的位置。具体来说，我们为离线培训构建了一种新颖的抽样策略。对流行的具有挑战性的数据集进行的实验表明，所提出的跟踪系统可以与最近开发的通用多对象跟踪方法相媲美，但所需的内存却少得多。此外，我们的跟踪系统通过GPU（CPU）可以以80（30）fps的速度运行，比大多数基于深度神经网络的跟踪器要快得多。我们发现，简单地提高检测性能可以带来更好的多对象跟踪结果。

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