Abstract
In this paper, the problem of multi-target tracking with single camera in complex scenes is addressed. A new approach is proposed for multi-target tracking problem that learns from hierarchy of convolution features. First fast Region-based Convolutional Neutral Networks is trained to detect pedestrian in each frame. Then cooperate it with correlation filter tracker which learns target’s appearance from pretrained convolutional neural networks. Correlation filter learns from middle and last convolutional layers to enhances targets localization. However correlation filters fail in case of targets full occlusion. This lead to separated tracklets (mini-trajectories) problem. So a post processing step is added to link separated tracklets with minimum-cost network flow. A cost function is used, that depends on motion cues in associating short tracklets. Experimental results on MOT2015 benchmark show that the proposed approach produce comparable result against state-of-the-art approaches. It shows an increase 4.5 % in multiple object tracking accuracy. Also mostly tracked targets is 12.9% vs 7.5% against state-ofthe- art minimum-cost network flow tracker.
Highlights
Multi-target tracking task is to estimate number of targets and their trajectories across multiple frames
The results show that cooperating Fast RCNN with multicorrelation filters tracker produce high precision and low missed detection rate
Multi-target tracking algorithm is proposed that exploit features from pretrained convolutional neural network
Summary
Multi-target tracking task is to estimate number of targets and their trajectories across multiple frames. It is a crucial problem in the field of computer vision. It is highly demanded in many computer application such as surveillance, human behavior analysis and augmented reality. It consists of two components: detection and data association between detections across frames. Data association step is challenging due to many reasons such as missed or faulty detections, short and long term occlusions and interactions between targets in crowded scenes. Most recent approaches in multi-target tracking have followed tracking-by-detection approach, where object detectors output are linked to build targets trajectories
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