Occluded Area Map for Object Tracking Re-identification

The system performs preliminary actions by maintaining coasting tracks that predict object positions during occlusion periods. Instead of immediately initiating computationally intensive re-identification when tracks are lost, the system uses preliminary coasting predictions to guide subsequent re-identification efforts, reducing the need for exhaustive feature matching by focusing computational resources on likely object locations and trajectories.

The tracking system segments the problem into distinct phases: active tracking, coasting (prediction during occlusion), and re-identification. By segmenting the computational workload across these phases and using simple coasting models during occlusion periods, the system reserves computational resources for re-identification only when necessary, rather than continuously performing expensive feature extraction and matching operations.

The system dynamically adjusts the coasting period duration based on object characteristics, scene context, and occlusion conditions. Rather than using a fixed prediction horizon, the coasting mechanism adapts its temporal scope to balance continuity maintenance with error accumulation, switching to re-identification when prediction uncertainty reaches thresholds specific to each tracking scenario.

The system incorporates feedback mechanisms where coasting predictions are continuously evaluated against new detections and scene context. When coasting errors are detected or when occlusion patterns suggest prolonged absence, the system triggers re-identification to correct accumulated errors, creating a feedback loop that maintains both continuity and accuracy.