DeepAI AI Chat
Log In Sign Up

Online Tracking Parameter Adaptation based on Evaluation

by   Duc Phu Chau, et al.

Parameter tuning is a common issue for many tracking algorithms. In order to solve this problem, this paper proposes an online parameter tuning to adapt a tracking algorithm to various scene contexts. In an offline training phase, this approach learns how to tune the tracker parameters to cope with different contexts. In the online control phase, once the tracking quality is evaluated as not good enough, the proposed approach computes the current context and tunes the tracking parameters using the learned values. The experimental results show that the proposed approach improves the performance of the tracking algorithm and outperforms recent state of the art trackers. This paper brings two contributions: (1) an online tracking evaluation, and (2) a method to adapt online tracking parameters to scene contexts.


page 5

page 6


Automatic Parameter Adaptation for Multi-object Tracking

Object tracking quality usually depends on video context (e.g. object oc...

Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers

This paper improves state-of-the-art on-line trackers that use deep lear...

A multi-feature tracking algorithm enabling adaptation to context variations

We propose in this paper a tracking algorithm which is able to adapt its...

The Second-place Solution for CVPR 2022 SoccerNet Tracking Challenge

This is our second-place solution for CVPR 2022 SoccerNet Tracking Chall...

Online Visual Tracking with One-Shot Context-Aware Domain Adaptation

Online learning policy makes visual trackers more robust against differe...

Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach

This paper proposes a probabilistic approach for the detection and the t...

Adaptive Algorithm and Platform Selection for Visual Detection and Tracking

Computer vision algorithms are known to be extremely sensitive to the en...