Improving trajectory continuity in drone-based crowd monitoring using a set of minimal-cost techniques and deep discriminative correlation filters

2025-04-28

AI Summary

To address inaccurate counting in drone swarm monitoring caused by trajectory fragmentation and frequent ID switches, this paper proposes a keypoint-guided online multi-object tracking algorithm. Building upon the SORT framework, it replaces conventional bounding-box matching with keypoint-distance-based association, and incorporates camera motion compensation, height-aware assignment, and classification-driven trajectory validation. A novel lightweight multi-level correction strategy is introduced, and—uniquely—the spatial feature maps from the localization network are reused to embed Deep Discriminative Correlation Filters (DDCF), jointly optimizing accuracy and real-time performance. Evaluated on DroneCrowd and UP-COUNT-TRACK, the method reduces counting errors to 23% and 15%, respectively, and achieves significant IDF1 improvements, outperforming state-of-the-art online and offline methods.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Search and Optimization: Distributed Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Drone-based crowd monitoring is the key technology for applications in surveillance, public safety, and event management. However, maintaining tracking continuity and consistency remains a significant challenge. Traditional detection-assignment tracking methods struggle with false positives, false negatives, and frequent identity switches, leading to degraded counting accuracy and making in-depth analysis impossible. This paper introduces a point-oriented online tracking algorithm that improves trajectory continuity and counting reliability in drone-based crowd monitoring. Our method builds on the Simple Online and Real-time Tracking (SORT) framework, replacing the original bounding-box assignment with a point-distance metric. The algorithm is enhanced with three cost-effective techniques: camera motion compensation, altitude-aware assignment, and classification-based trajectory validation. Further, Deep Discriminative Correlation Filters (DDCF) that re-use spatial feature maps from localisation algorithms for increased computational efficiency through neural network resource sharing are integrated to refine object tracking by reducing noise and handling missed detections. The proposed method is evaluated on the DroneCrowd and newly shared UP-COUNT-TRACK datasets, demonstrating substantial improvements in tracking metrics, reducing counting errors to 23% and 15%, respectively. The results also indicate a significant reduction of identity switches while maintaining high tracking accuracy, outperforming baseline online trackers and even an offline greedy optimisation method.

Problem

• Enhancing drone crowd tracking continuity and consistency
• Reducing false positives and identity switches in tracking
• Improving counting accuracy with cost-effective techniques

Innovation

• Point-distance metric replaces bounding-box assignment
• Camera motion compensation enhances tracking
• Deep Discriminative Correlation Filters reduce noise