Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques - Matthijs H. Zwemer (Sensors, 2021)
This paper presents a camera-based vessel-speed enforcement system based on two cameras. The proposed system detects and tracks vessels per camera view and employs a re-identification (re-ID) function for linking vessels between the two cameras based on multiple bounding-box images per vessel. Newly detected vessels in one camera (query) are compared to the gallery set of all vessels detected by the other camera. To train and evaluate the proposed detection and re-ID system, a new Vessel-reID dataset is introduced. This extensive dataset has captured a total of 2474 different vessels covered in multiple images, resulting in a total of 136,888 vessel bounding-box images. Multiple CNN detector architectures are evaluated in-depth. The SSD512 detector performs best with respect to its speed (<semantics>85.0%</semantics> Recall@95Precision at 20.1 frames per second). For the re-ID of vessels, a large portion of the total trajectory can be covered by the successful detections of the SSD model. The re-ID experiments start with a baseline single-image evaluation obtaining a score of <semantics>55.9%</semantics> Rank-1 (<semantics>49.7%</semantics> mAP) for the existing TriNet network, while the available MGN model obtains <semantics>68.9%</semantics> Rank-1 (<semantics>62.6%</semantics> mAP). The performance significantly increases with <semantics>5.6%</semantics> Rank-1 (<semantics>5.7%</semantics> mAP) for MGN by applying matching with multiple images from a single vessel. When emphasizing more fine details by selecting only the largest bounding-box images, another <semantics>2.0%</semantics> Rank-1 (<semantics>1.4%</semantics> mAP) is added. Application-specific optimizations such as travel-time selection and applying a cross-camera matching constraint further enhance the results, leading to a final <semantics>88.9%</semantics> Rank-1 and <semantics>83.5%</semantics> mAP performance.