# Tracking as A Whole: Multi-Target Tracking by Modeling Group Behavior with Sequential Detection

## TL;DR

This paper introduces a novel multi-target vehicle tracking framework that models group behavior and uses sequential detection with shape priors to improve accuracy in occluded, crowded scenes at road junctions.

## Contribution

It proposes a new tracking-by-detection method that incorporates shape priors and traffic force modeling to handle occlusions and complex vehicle interactions.

## Key findings

- Effective in occluded, crowded scenes
- Improves detection accuracy at road junctions
- Demonstrates robustness on real surveillance videos

## Abstract

Video-based vehicle detection and tracking is one of the most important components for Intelligent Transportation Systems (ITS). When it comes to road junctions, the problem becomes even more difficult due to the occlusions and complex interactions among vehicles. In order to get a precise detection and tracking result, in this work we propose a novel tracking-by-detection framework. In the detection stage, we present a sequential detection model to deal with serious occlusions. In the tracking stage, we model group behavior to treat complex interactions with overlaps and ambiguities. The main contributions of this paper are twofold: 1) Shape prior is exploited in the sequential detection model to tackle occlusions in crowded scene. 2) Traffic force is defined in the traffic scene to model group behavior, and it can assist to handle complex interactions among vehicles. We evaluate the proposed approach on real surveillance videos at road junctions and the performance has demonstrated the effectiveness of our method.