COA-SSD: A novel chaotic optimization algorithm for enhancement of real-time video surveillance images using single shot detector with MobileNetV2

Video surveillance involves the deployment of cameras to observe and record activities for security and monitoring purposes. This is vital for crime prevention, public safety, and improving operational efficiency across various environments. The rising demand for real-time security and threat detection has increased interest in sophisticated video surveillance systems. However, challenges persist in achieving an accurate automated analysis under diverse environmental and situational conditions. This paper presents an optimization strategy for video surveillance system deployment using a deep-learning algorithm. This strategy combines the base image with information about the behavioral events depicted in the frame sequence and then uses a Sobel filter to detect boundaries. The present study employs a rapid feature point identification algorithm known as the local-peak scale-invariant feature transform (LP-SIFT), which is based on multi-scale local peak features that are invariant to scale and represent multi-scale fluid turbulence. Traditional segmentation methods in video surveillance often lack contextual understanding and temporal consistency, which Modulated Memory Networks aim to address using adaptive memory-driven feature modulation. Next, we used an (SSD) with MobileNet V2 to classify fragments. SSDs can bypass the region proposal network and reduce latency. SSDs incorporate several optimizations such as multiple cooperative functions and base boxes to compensate for the loss of accuracy. To optimize and enhance the accuracy and efficiency of the video surveillance classification, the model parameters were fine-tuned. The Coati Optimization Algorithm (COA) is a metaheuristic algorithm inspired by coati behaviors: their cooperative strategy when attacking iguanas, and their anti-predator and avoidance behavior strategies. To evaluate the LP-SIFT algorithm, we considered datasets with varying scenes, pixel sizes, and distortion levels, across different lighting conditions and environments.