Multi-Hypothesis Testing Approach for Multi-Target Tracking with Trajectory Prediction
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Resource Overview
Implementation of multi-target tracking using the Multi-Hypothesis Testing method, featuring a video surveillance platform integrated with Kalman filtering for target trajectory prediction. Users can customize the codebase according to their specific requirements.
Detailed Documentation
This research employs the Multi-Hypothesis Testing (MHT) method to achieve robust multi-target tracking, successfully developing a comprehensive video surveillance platform. The system integrates a Kalman filter algorithm to predict target motion trajectories with probabilistic state estimation. The implementation includes key functions such as hypothesis generation, track management, and data association modules, with the Kalman filter providing recursive prediction-correction cycles for trajectory smoothing. We provide full source code access, enabling users to modify parameters like measurement noise covariance, gate thresholds, and hypothesis pruning criteria for customization. This work offers new insights for multi-target tracking research and applications, contributing to advancements in the field through its modular and adaptable code architecture.
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