Online Multiple Person Tracking Using Fully-Convolutional Neural Networks and Motion Invariance Constraints
We propose a novel framework for multiple person tracking in crowded scenes with the tracking-by-detection paradigm.In such scenes,noisy detections and frequent occlusions are major challenges.A common way to handle the challenges is to use Convolutional Neural Networks(CNNs)based appearance features to discriminate objects.However,to get sufficiently discriminative features,CNNs demand a large amount of training data and sometimes compromise efficiency.We address the challenges in two ways.Firstly,an Appearance Net modified from a Siamese network is proposed to identify persons in crowed scenes.Compared to other CNNs with deep layers and careful fine-tuning,our Appearance Net is efficient and accurate enough without any fine-tuning.Secondly,a motion invariance model is designed to tackle noisy detections caused by cluttered background or inaccurate bounding box localization,and missing objects caused by occlusions.By utilizing spatial geometric constraints,our tracker can generate reliable trajectories under challenging scenes.Extensive experiments on the two largest multi-object tracking(MOT)benchmarks,namely MOT15 and MOT17,demonstrate competing performance of the proposed tracker over a number of state-of-the-art trackers.
Fully-convolutional neural networks Motion invariance Multiple object tracking
Nan Wang Qi Zou Yaping Huang Qiulin Ma
Beijing Key Laboratory of Traffic Data Analysis and Mining,Beijing Jiaotong University,Beijing,China
国际会议
广州
英文
150-161
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）