Holistic Approach for Enhanced Object Recognition in Complex Environments

Addressing occlusion in object detection is a critical challenge as the use of smart video surveillance systems (SVS) grows. Current methods often struggle with relative occlusions among objects, which can severely compromise the efficacy of real-world SVS applications. Existing occlusion-handling strategies typically face limitations related to data inflexibility and the accurate discrimination of objects. To overcome these challenges, this paper introduces a sophisticated end-to-end solution that utilizes point cloud density analysis for precise occlusion rate assessment and combines depth information with RGB data to effectively separate occluded objects. This approach leverages advancements in integrating 2D and 3D data, employing cutting-edge network architectures and enhanced preprocessing techniques. Our model demonstrated its effectiveness on the KITTI dataset, particularly in scenarios where occlusion exceeded 20%. It surpassed the performance of YOLO3D by an average improvement of 10% in AP, and CompNet by 5%. This enhancement is indicative of the model’s advanced capabilities in discerning and accurately detecting objects, even when they are partially obscured, which is a substantial step forward in the realm of occlusion handling.