Real-Time Object Detection and Tracking Design Using Deep Learning with Spatial–Temporal Mechanism for Video Surveillance Applications

Abstract

We propose a CNN-based framework for “real-time object detection and tracking using deep learning” in this paper, which includes a spatial–temporal mechanism. The impact of efficient data on performance benchmarks in terms of accuracy has changed. The data processing is handled by industry buzzwords: deep learning (DL) and computer vision (CV). The CNN-based framework uses the single object tracker value to match arrival models and find targets in the next frame. Simply applying single object tracking to multiple object tracking will encounter problems in computational efficiency and results due to occlusion. In this paper, we introduce a “spatial attention mechanism (STAM)” to manage occlusion bias and target interaction. Object tracking is a sensational technology in image processing with great future implications. Multiple object tracking (MOT) has seen an extensive boom in the last few years due to machine learning, deep learning, computer vision, and more. This paper aims to provide an object tracking software solution. Using YOLO’s “You Only Look Once” technology with the help of Tensor flow, the system is geared toward object detection, tracking, and counting. Proven, effective detection and tracking on various dataset. Algorithms that offer real-time, accurate, and precise identifications appropriate for real-time applications.