Automatic Parameter Adaptation for Multi-object Tracking

TL;DR

This paper introduces a learning-based method for automatically adapting multi-object tracking parameters to varying video contexts, improving tracking performance across different scenarios.

Contribution

It presents a novel classification approach for offline parameter learning and a new online tuning method based on detected context changes.

Findings

Outperforms recent state-of-the-art trackers

Effective classification of video contexts for parameter learning

Successful online parameter tuning during tracking

Abstract

Object tracking quality usually depends on video context (e.g. object occlusion level, object density). In order to decrease this dependency, this paper presents a learning approach to adapt the tracker parameters to the context variations. In an offline phase, satisfactory tracking parameters are learned for video context clusters. In the online control phase, once a context change is detected, the tracking parameters are tuned using the learned values. The experimental results show that the proposed approach outperforms the recent trackers in state of the art. This paper brings two contributions: (1) a classification method of video sequences to learn offline tracking parameters, (2) a new method to tune online tracking parameters using tracking context.