Deep Learning-Based Camouflaged Object Detection and Tracking for Enhanced Video Surveillance

To improve security and tactical awareness, video surveillance systems need to be able to find and track objects that are hidden. The approach suggests a deep learning-based method for finding and following hidden objects. There are two major steps in our method: finding and following. During the detecting step, we use a R-CNN based model to find items that are hidden in each frame. A lot of labelled pictures with different kinds of camouflage patterns and objects are used to train this model. The updated Faster R-CNN has more levels and changes that make it better at finding things that are hidden. To keep track of the objects across frames, we use an LSTM-based tracker. The LSTM tracker uses the object's traits and motion information to keep track of its state and guess where it will be in the next frame. To make our method even more effective, we use pre-trained models like MobileNet and VGG-19 for feature extraction. This makes the computations simpler while keeping the accuracy of the recognition and tracking high. The proposed model tested on a number of difficult video scenes with items that were well hidden. The results show that our method works better than other methods, as it increases the accuracy of both recognition and tracking. Also, our method works well even when the lighting changes, the size of the objects changes, or the background is complicated. The suggested method based on deep learning looks like a good way to find and follow hidden objects in video monitoring scenarios. Using CNNs, LSTM networks, and pre-trained models like MobileNet and VGG-19, we show big improvements in terms to compare the various performance parameters with accuracy and stability, which helps make security and monitoring systems better.