Hybrid Techniques for Object Detection and Tracking: A Review

In this paper, a detailed analysis about a hybrid object detection and tracking framework that combine traditional image processing methods with modern deep learning models has been discussed. Techniques such as Kalman filtering, optical flow, and deep networks like YOLO and Faster R-CNN are combined to improve performance in real-time, multi-object scenarios. Some key challenges including occlusion, identity preservation, and environmental variability have been discussed in this paper. Some datasets like Pascal VOC, ImageNet, Microsoft COCO, etc. and parametric evaluations like precision, accuracy, mAP and MOTA etc. have been studied.