Head Anchor Enhanced Detection and Association for Crowded Pedestrian Tracking

TL;DR

This paper introduces a novel pedestrian tracking framework that enhances detection and association by incorporating head keypoints, richer features, and an improved motion model to better handle occlusions in crowded scenes.

Contribution

It proposes a comprehensive tracking method combining head keypoint detection, multi-branch feature embedding, and an iterative Kalman filter with 3D priors for improved occlusion robustness.

Findings

Improved tracking accuracy in crowded, occluded scenes.

Enhanced feature representation from detection branches.

Robust motion modeling with iterative Kalman filtering.

Abstract

Visual pedestrian tracking represents a promising research field, with extensive applications in intelligent surveillance, behavior analysis, and human-computer interaction. However, real-world applications face significant occlusion challenges. When multiple pedestrians interact or overlap, the loss of target features severely compromises the tracker's ability to maintain stable trajectories. Traditional tracking methods, which typically rely on full-body bounding box features extracted from {Re-ID} models and linear constant-velocity motion assumptions, often struggle in severe occlusion scenarios. To address these limitations, this work proposes an enhanced tracking framework that leverages richer feature representations and a more robust motion model. Specifically, the proposed method incorporates detection features from both the regression and classification branches of an object detector, embedding spatial and positional information directly into the feature representations. To further mitigate occlusion challenges, a head keypoint detection model is introduced, as the head is less prone to occlusion compared to the full body. In terms of motion modeling, we propose an iterative Kalman filtering approach designed to align with modern detector assumptions, integrating 3D priors to better complete motion trajectories in complex scenes. By combining these advancements in appearance and motion modeling, the proposed method offers a more robust solution for multi-object tracking in crowded environments where occlusions are prevalent.