# Multi-Class Multi-Object Tracking using Changing Point Detection

arXiv: https://arxiv.org/abs/1608.08434 · HTML: https://mlsanity.com/paper/1608.08434/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2016.

## Built on by (most cited first, 2 of 2)

- [PoseDet: Fast Multi-Person Pose Estimation Using Pose Embedding](https://mlsanity.com/paper/2107.10466/) (2021)
- [Illumination Variation Correction Using Image Synthesis For Unsupervised Domain Adaptive Person Re-Identification](https://mlsanity.com/paper/2301.09702/) (2023)

## Abstract (arXiv)

This paper presents a robust multi-class multi-object tracking (MCMOT) formulated by a Bayesian filtering framework. Multi-object tracking for unlimited object classes is conducted by combining detection responses and changing point detection (CPD) algorithm. The CPD model is used to observe abrupt or abnormal changes due to a drift and an occlusion based spatiotemporal characteristics of track states. The ensemble of convolutional neural network (CNN) based object detector and Lucas-Kanede Tracker (KLT) based motion detector is employed to compute the likelihoods of foreground regions as the detection responses of different object classes. Extensive experiments are performed using lately introduced challenging benchmark videos; ImageNet VID and MOT benchmark dataset. The comparison to state-of-the-art video tracking techniques shows very encouraging results.