Kalman Filter Based Multiple Person Head Tracking

TL;DR

This paper introduces a simple target representation for multi-person head tracking using Kalman filters, focusing on head localization to improve tracking efficiency amidst occlusions and deformations, evaluated on challenging datasets.

Contribution

Proposes a head-based tracking method utilizing Kalman filters and combinatorial optimization, reducing complexity compared to deep learning approaches.

Findings

Effective head localization in challenging scenarios

Achieved promising results on surveillance datasets

Reduced computational complexity compared to deep learning methods

Abstract

For multi-target tracking, target representation plays a crucial rule in performance. State-of-the-art approaches rely on the deep learning-based visual representation that gives an optimal performance at the cost of high computational complexity. In this paper, we come up with a simple yet effective target representation for human tracking. Our inspiration comes from the fact that the human body goes through severe deformation and inter/intra occlusion over the passage of time. So, instead of tracking the whole body part, a relative rigid organ tracking is selected for tracking the human over an extended period of time. Hence, we followed the tracking-by-detection paradigm and generated the target hypothesis of only the spatial locations of heads in every frame. After the localization of head location, a Kalman filter with a constant velocity motion model is instantiated for each target that follows the temporal evolution of the targets in the scene. For associating the targets in the consecutive frames, combinatorial optimization is used that associates the corresponding targets in a greedy fashion. Qualitative results are evaluated on four challenging video surveillance dataset and promising results has been achieved.