Auto Object Detection and Tracking Using Trajectory Matching in Test Range Scenarios

Effective object detection as well as tracking is crucial in Range tracking applications, especially when dealing with high-speed targets such as ballistic objects or jettisoned pieces from a fast-moving mother vehicle in an occlusion environment during initial acquisition and tracking. This paper addresses the challenges of detection and tracking such objects during distraction, particularly for rapid detection by differentiating target-of-interest from non-relevant objects such as birds. To overcome the limits of conventional classification methods while dealing with shape and motion inconsistencies in real-time scenarios, a hybrid approach is proposed for automatic detection and tracking based on trajectory matching. Using simulated datasets of predefined trajectories, the proposed method automatically detects the target-of-interest and ensures accurate tracking in occlusion scenarios, even among multiple objects. Experimental results with the suggested approach have also shown robustness and dependency, as demonstrated by giving a 91% successful rate in correctly detecting no false positives in target trajectory recognition.