Cross-View Detection of Crowded Objects Based on Multi-Sensor Fusion

Traditional object detection methods are limited by single-sensor constraints, high computational requirements, and poor real-time performance. In addition, occlusion often occurs under the condition of restricted single-view. In this paper, we introduce a camera and LiDAR fusion-based object detection method, which achieves excellent detection performance under limited computational resources. We also explores a fusion detection method deployed with multi-view, which can effectively solve the occlusion issue encountered by single view. The proposed method is valuable for single view as well as multi-view in various application scenarios. Our fusion method significantly improves detection accuracy and reliability, and solves the problems of data discrepancy, interference between sensors, and occlusion due to restricted view. Simulations and extensive experiments show that our proposed object detection method exhibited high accuracy and relatively low computational time.