Efficient Learning Video Surveillance and Tracking Based on Deep Learning Algorithms

Video surveillance systems play a crucial role in various fields, including public safety, building management, industrial control, and road traffic. However, traditional systems heavily rely on manual monitoring and lack intelligent processing capabilities, leading to inefficiencies and delayed responses in emergencies. To address these challenges, this study proposes a deep learning-based video surveillance and tracking system. The system incorporates C3D (3D ConvNets) for feature extraction, a residual structure to enhance learning, and the 3D SENet (Squeeze and Excitation Networks) module to improve detection accuracy. Large-scale video datasets from urban street scenes, shopping malls, and factories were utilized for data preprocessing, enhancement, and classification. Experimental results indicate that in factory environments, the proposed system achieved the highest detection accuracy of 92.3% and a false alarm rate of only 3%. Comparatively, this system outperformed traditional methods across diverse scenarios, highlighting its enhanced intelligence and efficiency. Furthermore, the system demonstrated stable performance under varying lighting and weather conditions, proving its reliability in complex environments. These findings validate the potential of integrating deep learning techniques to revolutionize video surveillance systems, offering superior detection accuracy and robust adaptability.