Enhanced Multi-Object Detection via the Integration of PSO, Kalman Filtering, and CNN Compressive Sensing

Abstract

Many inventive techniques have been created in the field of machine vision to solve the challenging challenge of detecting and tracking one or more objects in the face of challenging conditions, such as obstacles, object motion, changes in light, shaking, and rotations. This research article provides a novel method that combines Convolutional Neural Networks (CNNs), Compressive Sensing, Kalman Filtering, and Particle Swarm Optimization (PSO) to address the challenges of multiobject tracking under dynamic conditions. Initially, a CNN-based object classification and identification system is demonstrated, which efficiently locates objects in video frames. Subsequently, in order to produce precise representations of object appearances, utilizing compressive sensing techniques. The Kalman Filter ensures adaptability to irregular observations, eliminates erroneous data, and reduces uncertainty. PSO enhances tracking efficiency by optimizing forecast precision. When combined, these techniques provide robust tracking even in the presence of complex movement patterns, occlusions, and visual disparities.The efficiency of this strategy is demonstrated by an empirical investigation that produces a remarkable tracking accuracy of 98%, which is 3.15% greater than other methods across a range of challenging settings. This technique has been compared to various existing approaches, including the Clustering Method, YOLOV4 DNN Model, and YOLOV3 Model, and its deployment is made easier with Python software. This hybrid technique, which addresses the limitations of separate approaches and offers a holistic approach to multi-object monitoring, has potential applications in surveillance, robotics, and autonomous systems.