Abstract: The present invention relates to a method for thermal video surveillance based on an Encoder-Decoder-induced feature pooling module. The method is explained as follows: An input thermal image that is to be processed, by a pre-trained ResNet-152 deep learning network, wherein the network comprises several convolutional layers, batch normalization layers, and a rectified linear unit (ReLU) function to extract features at low, mid and high levels, and the in-depth target features; receiving, by a feature pooling module (FPM), from the deep learning network, wherein the feature pooling module comprises of a max pooling layer, a convolutional layers, and various atrous convolutional layers for extracting the target features in higher dimensional features space at multi-scales; obtaining higher dimensional features space by a decoder network, wherein the decoder network is configured to project the higher dimensional features into image space for the generation of a probability mask.

Abstract: An image enhancement and object detection system is provided to enhance the degraded images and bring out the inherent details of the image received via an underwater surveillance system. The said system includes a novel deep learning architecture i.e., a Zero-DCE-U that enhances the degraded underwater images using modified UIQM. Further, a new training method using a single image based on correlation is employed. Furthermore, a G-UNet architecture is configured to detect moving objects while preserving the spatial-contextual relationships.

Abstract: An action detection system for dark or low-light videos is provided to detect human action recognition in dark-light situations. The said system includes a novel deep learning architecture that involves an image enhancement module configured to enhance the low-light image frame of action video sequence followed by an action classification module, to classify the actions from the 3D features extracted from the enhanced image frames.