Image processing and the analysis techniques are the increasing attention when they have enabled the non-extractive and the non-lethal approach for the collection of fisheries data. The data collection includes the following requirements such as fish size, catch estimation, regulatory compliance, species recognition and population counting. The main process that is used to measure the size of fish accurately is image segmentation. The challenges that can affect the segmentation of images include the blurring of the image areas due to the water droplets on the camera lens and the fish bodies which are out of the camera view. This project describes the automatic segmentation of fish for underwater images This segmentation algorithm implemented for identify the shape of the fish contour-based segmentation is implemented in this project. The project describes about the issues with an effective contour-based segmentation from an initial segmentation. The refinement is processed from coarse level to fine level. At the coarse level, the entire fish is aligned for the contour of the initial segmentation with trained representative contours by using iteratively reweighted least squares (IRLS). At finer levels, the refinement of contour segments is done to represent poorly segmented or missing shape parts. This method addresses the problems listed above and generates promising results with highly robust segmentation performance and length measurement.

Anomaly detection is a challenging task in the surveillance system due to the factors like extracting appropriate features, inappropriate differentiation among the normal vs abnormal behaviours, the sparse occurrence of abnormal activities and environmental variations. In the dark environment, detection of human actions is still difficult as more features for recognizing the key point are not visible. Hence the proposed work is focused on overcoming the environmental variations task that too in a less bright environment by using thermal videos. Variations in the actions can be easily identified as it works on the property of infrared radiations. For recognizing actions, the skeleton-based approach is used as it helps with the joint-wise segregation of human parts, resulting in more accuracy. The motion pattern of humans in the thermal video is tracked to classify the level of abnormality.

The channel allocation is the primary concept for enhancing the throughput and channel quality. The target channel allocation will enhance the performance by minimizing the noise rate as the resource utilization leads to an important problem for primary user. The dynamic channel allocation technique is maintained in this paper through the multi-objective optimization technique. The poor resource allocation leads to desperate problem in the wireless network, the proposed technique shows the improved throughput and energy proficiency.