Deep Continuous Conditional Random Fields With Asymmetric Inter-Object Constraints for Online Multi-Object Tracking

Online multi-object tracking (MOT) is a challenging problem and has many important applications including intelligence surveillance, robot navigation, and autonomous driving. In existing MOT methods, individual object's movements and inter-object relations are mostly modeled separately and relations between them are still manually tuned. In addition, inter-object relations are mostly modeled in a symmetric way, which we argue is not an optimal setting. To tackle those difficulties, in this paper, we propose a deep continuous conditional random field (DCCRF) for solving the online MOT problem in a track-by-detection framework. The DCCRF consists of unary and pairwise terms. The unary terms estimate tracked objects' displacements across time based on visual appearance information. They are modeled as deep convolution neural networks, which are able to learn discriminative visual features for tracklet association. The asymmetric pairwise terms model inter-object relations in an asymmetric way, which encourages high-confidence tracklets to help correct errors of low-confidence tracklets and not to be affected by low-confidence ones much. The DCCRF is trained in an end-to-end manner for better adapting the influences of visual information as well as inter-object relations. Extensive experimental comparisons with state-of-the-arts as well as detailed component analysis of our proposed DCCRF on two public benchmarks demonstrate the effectiveness of our proposed MOT framework.

I. Introduction

Robust tracking of multiple objects [1] is a challenging problem in computer vision and acts as an important component of many real-world applications. It aims to reliably recover trajectories and maintain identities of objects of interest in an image sequence. State-of-the-art Multi-Object Tracking (MOT) methods [2], [3] mostly utilize the tracking-by-detection strategy because of its robustness against tracking drift. Such a strategy generates per-frame object detection results from the image sequence and associates the detections into object trajectories. It is able to handle newly appearing objects and is robust to tracking drift. The tracking-by-detection methods can be categorized into offline and online methods. The offline methods [4] use both detection results from past and future with some global optimization techniques for linking detections to generate object trajectories. The online methods, on the other hand, use only detection results up to the current time to incrementally generate object trajectories. Our proposed method focuses on online MOT, which is more suitable for real-time applications including autonomous driving and intelligent surveillance.