## 1 Introduction
This approach allows for the integration of temporal and spatial details in the same convolution operation. One of the most important parts of object identification is identifying the kind of item present in an image or video and pinpointing its exact location with the help of a bounding box [17]. However, this method poses challenges that can be addressed through the use of deep learning techniques. Popular frameworks that are utilized include Deep Belief Networks, CNNs, and Recurrent Neural Networks (RNNs), which are commonly used in processing temporal signals. Systems that rely on visual understanding need segmentation modules. Since the discovery of the computer vision field, image segmentation has been a primary challenge related to the separation of images into several segments [18]. Segmentation denotes a method that involves allocating labels to every image pixel, based on the understanding that images sharing a label are generally linked to the same object. Several objects may be included when segmenting an image, such as buildings, dogs, cars, or people. When image segmentation is accurate, understanding scenes becomes simpler [19]. As a computer vision problem, human segmentation has been used in numerous applications, including understanding human movement, retexturing and classifying clothing, or identifying pedestrians. Human segmentation is a crucial phase of surveillance cameras' functions before any recognition decision. However, this is often challenging because the human body shape, pose, environment, and clothing differ from one instance to the next. To successfully segment the human body from its background, the human segmentation approach needs the capability to define the boundaries of the human body and mask them. The image segmentation task can broadly be handled using two methods. The first technique identifies similar image pixels from the segment. This detected similarity depends on pixel threshold values and can be attained in ML through clustering algorithms. The second technique uses the image's pixel discontinuity. Some methods for detecting the edge, point, and line employ the discontinuity approach to obtain intermediate segments. The ultimate segmentation image can be obtained by further processing the intermediate segments. To date, the available algorithms designed to solve the image segmentation problem apply varying techniques based on neural networks, clustering, region, edge, and threshold. This work mainly solves the semantic segmentation problem using a region-based technique [20]. In semantic segmentation, pictures are categorized pixel by pixel such that each pixel belongs to a unique class cluster [21]. With the advent of deep learning, semantic segmentation has become a pivotal area of computer vision and image processing; it has experienced major research efforts and diverse applications in various fields [22]. Instance segmentation is a particular type of image segmentation that focuses on identifying objects' instances and outlining their boundaries. It has broad practical use in various real-world situations, such as self-driving vehicles, medical imaging, and monitoring crops from above. When there are several objects of the same kind that need to be tracked individually, instance segmentation is useful. Image fusion combines multiple images from different sources into a single image containing information from all of the input images. Image fusion aims to create a new image with improved features compared to the individual input images. This can include improved resolution, increased contrast, or reduced noise. The technique is used in many fields, including remote sensing, medical imaging, and surveillance. Deep learning has been used for image fusion to improve the performance of the fusion process. The utilization of CNNs enables the extraction of characteristics from the input images, which can then be combined through a fusion process.