Adaptive Neuro-Fuzzy Controller for Multi-Object Tracker

Abstract : Sensitivity to scene such as contrast and illumination intensity, is one of the factors significantly affecting the performance of object trackers. In order to overcome this issue, tracker parameters need to be adapted based on changes in contextual information. In this paper, we propose an intelligent mechanism to adapt the tracker parameters, in a real-time and online fashion. When a frame is processed by the tracker, a controller extracts the contextual information, based on which it adapts the tracker parameters for successive frames. The proposed controller relies on a learned neuro-fuzzy inference system to find satisfactory tracker parameter values. The proposed approach is trained on nine publicly available benchmark video data sets and tested on three unrelated video data sets. The performance comparison indicates clear tracking performance improvement in comparison to tracker with static parameter values, as well as other state-of-the art trackers.
Complete list of metadatas

Cited literature [17 references]  Display  Hide  Download

https://hal.inria.fr/hal-01164734
Contributor : Duc Phu Chau <>
Submitted on : Wednesday, July 8, 2015 - 1:52:22 PM
Last modification on : Thursday, February 7, 2019 - 5:08:20 PM
Long-term archiving on : Friday, October 9, 2015 - 10:05:39 AM

Files

paperICVS15.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01164734, version 1
  • BIBCODE : dpchau

Collections

Citation

Duc Phu Chau, Kartick Subramanian, François Bremond. Adaptive Neuro-Fuzzy Controller for Multi-Object Tracker. 10th International Conference on Computer Vision Systems, Jul 2015, Copenhagen, Denmark. ⟨hal-01164734⟩

Share

Metrics

Record views

326

Files downloads

385