MATLAB Implementation of MTT (Multi-Target Tracking) Algorithm

MTT (Multi-Target Tracking) algorithm is a technique used for simultaneously tracking multiple dynamic targets. This algorithm solves tracking challenges in complex scenarios such as target crossing and occlusion by associating observational data across time domains.

Implementing MTT algorithm in MATLAB typically involves the following core steps:

Target Detection and Feature Extraction First, target detection must be performed on each frame to extract target features (such as position, velocity, shape). Common methods include background subtraction, optical flow, or deep learning detectors (like YOLO, Faster R-CNN). In MATLAB implementation, this can be achieved using Computer Vision Toolbox functions like detectORBFeatures or deep learning frameworks through importNetworkFromTensorFlow.

Data Association The core of multi-target tracking lies in solving the association problem between observational data and target trajectories. Common association methods include Kalman Filter combined with Hungarian Algorithm (for optimal matching between observations and predicted trajectories) or more complex Joint Probabilistic Data Association (JPDA). MATLAB provides assignkbest and assignauction functions for implementing assignment algorithms, while kalmanFilter objects handle state prediction and correction.

Trajectory Management Maintaining each target's trajectory status (such as confirmed tracks, tentative tracks, or lost tracks) requires handling new target appearances and old target disappearances. Typically, logical rules need to be established, such as marking a target as lost if unmatched for N consecutive frames. This can be implemented using track management classes that update track states based on association results.

Temporal Fusion and Prediction Using Kalman Filter or Particle Filter for target state prediction and correction, combined with current frame observation data to improve tracking stability. MATLAB's trackingKF and trackingPF classes provide built-in implementations for these filtering approaches with configurable motion models.

Multi-Point Multi-Time Association For complex scenarios, multi-sensor data fusion or long-term association strategies may be necessary (such as using graph models or deep learning for cross-frame matching). The Sensor Fusion and Tracking Toolbox offers multiObjectTracker and trackFuser classes for handling multi-sensor scenarios.

MTT algorithm performance highly depends on parameter tuning (such as association thresholds, motion model selection) and scenario adaptation. Related research papers typically discuss the robustness of different association strategies and optimization methods under occlusion and dense target conditions.

For specific implementations, recommended reference papers include: Kalman Filter-based Multi-Target Tracking Deep Learning Applications in MTT (such as SORT, DeepSORT algorithms) Distributed Tracking with Multi-Sensor Data Fusion