Real-Time Moving Object Detection With Intruder Alert System for Enhanced Security

Object detection has gained significant research interest in recent years due to its close connection with video analysis and image comprehension. Traditional approaches to object detection rely on handcrafted features and shallow trainable architectures. However, their performance tends to plateau, often requiring complex ensemble models that integrate multiple low-level image features with high-level contextual information from object detectors and scene classifiers. With advancements in deep learning, more sophisticated techniques capable of learning high-level semantic representations have emerged to overcome the limitations of traditional methods. These modern models differ in aspects such as network architecture, training strategies, and optimization functions. This paper presents Python-based object detection frameworks, highlighting key generic architectures and discussing modifications and techniques that enhance detection performance. Additionally, as different detection tasks possess unique characteristics, we briefly explore specialized applications such as salient object detection, face detection, and pedestrian detection. Furthermore, we provide experimental analyses comparing various methods and extracting insightful conclusions. Finally, we outline promising research directions and future challenges in object detection and deep learningbased recognition systems

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