Motion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object robustly. Besides current research on moving object detection from UAV aerial images mostly depends on either frame difference or segmentation approach separately. There are two main purposes for this research: firstly to develop a new motion model called DMM (dynamic motion model) and secondly to apply the proposed segmentation approach SUED (segmentation using edge based dilation) using frame difference embedded together with DMM model. The proposed DMM model provides effective search windows based on the highest pixel intensity to segment only specific area for moving object rather than searching the whole area of the frame using SUED. At each stage of the proposed scheme, experimental fusion of the DMM and SUED produces extracted moving objects faithfully. Experimental result reveals that the proposed DMM and SUED have successfully demonstrated the validity of the proposed methodology.

Automated Vehicular System has become a necessity in the current technological revolution. Real Traffic sign detection and recognition is a vital part of that system that will find roadside traffic signs to warn the automated system or driver beforehand of the physical conditions of roads. Mostly, researchers based on Traffic sign detection face problems such as locating the sign, classifying it and distinguishing one sign from another. The most common approach for locating and detecting traffic signs is the color information extraction method. The accuracy of color information extraction is dependent upon the selection of a proper color space and its capability to be robust enough to provide color analysis data. Techniques ranging from template matching to critical Machine Learning algorithms are used in the recognition process. The main purpose of this research is to give a review based on methods and framework of Traffic Sign Detection and Recognition solution and discuss also the current challenges of the whole solution.

Motion analysis for moving object from UAV aerial images is still an unsolved issue in computer vision research field due to fast abrupt motion of object and UAV, low resolution, noisy imagery, cluttered background, low contrast and small target size. The main reason for the inability to handle motion is the weakness of existing approaches for moving object detection. This paper presents critical analysis of the various methods used for motion analysis which states lack of relevancy with motion analysis along with some unsolved problems need to be solved for optimum performance of moving objects detection from UAV aerial images. The overall reviews proposed in this paper have been extensively studied in various research papers which can significantly contribute to computer vision research and can be potential for future development and direction for future research.

Corner feature based moving objects detection is an essential and fundamental research problem in the broader aspects of computer vision and pattern recognition research domain. Performance of various corner features based aerial types image processing specially for moving objects detection is still an unsolved issue due to up and down of performance which makes it difficult to choose the appropriate corner features for detection purpose. The core part mentioned in this research is to categorize significant corner characteristics of the objects using various corner features based detection methods in image extracted from aerial video. This research demonstrated three kinds of corner features, i.e. Moravec, Susan and Harris corners due to capability of these corner features to interpret high and low intensity various for aerial types of images. Standard datasets were used to evaluate each of the corner feature based detection. Based on comprehensive experimental analysis, Harris corner was observed performing efficiently comparing with Moravec and Susan corner based detection for both datasets considered by this research. Experimental results reveals the capacity of each corner characteristics based detection methodology in terms with the effectiveness using various performance metrics for moving object detection using aerial images.

Human action recognition has been a talked topic since machine vision was coined. With the advent of neural networks and deep learning methods, various architectures were suggested to address the problems within a context. Convolutional neural network has been the primary go-to architecture for image segmentation, flow estimation and action recognition in recent days. As the problem itself is an extended version of various subproblems, such as frame segmentation, spatial and temporal feature extraction, motion modeling and action classification as a whole, some methods reviewed in this paper addressed sub-problems and some tried to address a single architecture to the action recognition problem. While being a success, convolution neural networks have drawbacks in its pooling methods. CapsNet, on the other hand, uses squashing function to determine the activation. Also it addresses spatiotemporal information with the normalized vector maps while CNN-based methods extracts feature map for spatial and temporal information and later augment them in a fusion layer for combining two separate feature maps. Critical review of papers provided in this work can contribute significantly in addressing human action recognition problem as a whole.