Fusion-driven deep feature network for enhanced object detection and tracking in video surveillance systems

Abstract

Object detection and tracking (ODT) is a crucial research area in video surveillance (VS) systems and poses a significant challenge in computer vision and image processing. The primary objective is to identify objects of various classes from video sequences for detection and tracking. The object detection and tracking process involves extracting moving objects from frames and tracking them over time. With the advancement of computer intelligence, this study proposes a novel optimized deep fused learning (ODFL) model for efficient object detection and tracking in video surveillance systems. The optimized deep fused learning model focuses primarily on detecting and tracking multiple objects present in video frames. Initially, the input video undergoes pre-processing by converting it into video frames and applying Gaussian filtering to eliminate noise and enhance frame quality. Next, the feature fusion model employs the Dense Convolution Feature Fusion Network (D-ConvFFN) to extract and fuse relevant features. Subsequently, the Enhanced RefineDet-based Fire Hawk (ERFH) object detection module is utilized by the optimized deep fused learning model to efficiently recognize multiple objects in the video frames. The hyperparameters are optimized using the Fire Hawk optimizer. Once the objects are recognized, a softmax classifier categorizes them into different classes. The Hungarian-based SORT model is used for multi-object tracking. The proposed optimized deep fused learning model is implemented in Python, evaluated on the PETS S2 2009 and UA-DETRAC datasets, and assessed in terms of various evaluation measures. The optimized deep fused learning model’s performance is compared with prevailing architectures, and it achieves a maximum accuracy of 99.16% for the PETS S2 2009 dataset and 99.42% for the UA-DETRAC dataset, outperforming existing classifiers for multi-class object detection and tracking.