Mathematical Models Using Artificial Intelligence for Surveillance Systems

9Study of Traditional, Artificial Intelligence and Machine Learning Based Approaches for Moving Object Detection

Apoorv Joshi¹, Amrita², Rohan Sahai Mathur¹*, Nitendra Kumar¹ and Padmesh Tripathi³

¹Amity Business School, Amity University, Noida, Uttar Pradesh, India

²Center for Cyber Security and Cryptology, Computer Science & Engineering, Sharda School of Engineering & Technology, Sharda University, Greater Noida, Uttar Pradesh, India

³Delhi Technical Campus, Greater Noida, Uttar Pradesh, India

Abstract

The automated detection and tracking of objects in motion from visual data like images and videos is a classic problem in computer vision that has gained renewed interest and seen great progress with modern Artificial Intelligence (AI) and Machine Learning (ML) techniques. In particular, deep neural networks have shown unmatched capabilities for visual recognition tasks like classifying, localizing and segmenting objects of interest in complex scenes. A key advancement that has enabled the success of these techniques is the development of convolutional neural network (CNN) for moving object detection (MOD). Various architectures based on CNN can identify multiple objects in an image and draw bounding boxes around them. These networks can be trained on large annotated datasets to learn rich visual features automatically, eliminating the need for hand-coded feature extraction. Pre-trained networks can also be fine-tuned for specific use cases involving detection of relevant ...