Mathematical Models Using Artificial Intelligence for Surveillance Systems

8A Deep Learning System for Deep Surveillance

Aman Anand, Rajendra Kumar*, Nikita Verma, Akash Bhasney and Namita Sharma

School of Engineering and Technology, Sharda University, Greater Noida, India

Abstract

Deep surveillance is an essential task in computer vision that involves monitoring and analyzing video data to detect and track objects of interest, identify unwanted events, and ensure public safety. Analysis of real-time data has gained popularity in the last 10 years due to its remarkable results analyzing videos and interpreting pictures in many ways. By combining a variety of low-level picture characteristics with comparatively high-level information from object detectors and scene classifiers, they may easily stagnate their performance. This chapter presents a deep learning model with different implementations of convolutional neural networks (CNNs) for deep surveillance applications. The proposed model leverages the power of SoftMax Regression, Support Vector Machine, Convolutional Neural Network, MatConvNet, and Spatially-sparse CNN to achieve robust object detection, tracking, and anomaly detection from real-time video streams. The model incorporates both spatial and temporal information for comprehensive analysis and integrates various architectural innovations for improved performance. The mathematical model uses coordinate hashing for mapping the coordinates in different hierarchical layers of the convolution process for object detection. The Hash function ...