Image segmentation is very important and challenging process of image processing. It is an essential step in image analysis, object representation, visualization, and many other image processing tasks. It is the technique that divides the image into different segments. The different segmentation Techniques are Threshold based segmentation: In this method, image pixels are divided with the help of intensity level of an image. This method is mainly used to distinguish the foreground objects from background images.
Moreover, it also detects and reports any anomaly in a human gait in real time mode. The challenges involved in gait recognition include also imperfect foreground segmentation of the walking subject from the background scene and variations in the camera viewing angle with respect to the walking subjects. 1.6 Gait Detection Detection and tracking humans from a video sequence is the first step in gait recognition, where an individual is actually spotted walking. Gait detection systems usually work with the assumption that the video sequence to be processed is captured by a static camera, and the only moving object in video sequence is the subject (person). Given a video sequence from a static camera, this module detects and tracks the moving silhouettes.
These characteristics make big data a great challenge for organizations to learn useful knowledge from it. One of the essential characteristics of Big Data is the vast volume of data represented by different and varied dimensionalities. This is because the ones who collect information select their own procedure for data recording, and the nature of diverse applications also results in diverse data illustrations. Autonomous data sources with distributed and decentralized controls are a principal characteristic of Big Data applications. The enormous volumes of the data also make an application susceptible to spasms or malfunctions if the entire system has to be reliant on any centralized control entity.
Hand Gesture recognition using Hidden Markov Models Arpit Sharma D.A.V.I.E.T, Jalandhar firstname.lastname@example.org Abstract— With recent technological advancements in the field of artificial intelligence, human gesture recognition has gained special interests in the fields of computer vision and human computer interaction. Gesture recognition is a difficult problem because of the spatiotemporal variations in subject location, size in the frame. Subject occlusion also hinders the process of image recognition. Similarly, human gestures are a pattern recognition problem. This paper proposes a method to recognize time – varying human gestures from continuous video streams.
Dehghan et al. (2013) presented a method to handle fully or partial occlusion by preforming part based detection, but instead of taking the score of all the parts it considers the detection of full body, upper body and head only. To form the trajectories of tracking, a video is divided into segments in order to find people tracklets, and then merge them to find the trajectories using Generalized Minimum Clique Problem (GMCP). 220.127.116.11 Crowd behavior understanding Studying the actions of a crowd is a significant topic in computer vision and still under research. crowd behavior serves a great variety of such as detecting abnormal motions, crowd direction and speed which used further to predict the behavior in a specific environment at a particular time to help in a better crowd management.
Abstract- License Plate Recognition (LPR) plays a major role in this busy world. In this paper we have developed algorithms and MATLAB programs to efficiently recognize the license plate number. License Plate Recognition (LPR) system basically consists of three main processing steps such as Pre-processing of the number plate, Segmentation of the number plate and Recognition of the characters. Among this, character segmentation is the most challenging task as the accuracy of the character recognition relies on the accuracy of character segmentation. INTRODUCTION LPR (License Plate Recognition) is an application image processing used to identify vehicles by the license plate numbers.
After a face has been detected, the task of feature extraction is to obtain features that are fed into a face classification system. Depending on the type of classification system, features can be local features such as lines or facial features such as eyes, nose, and mouth. Face detection may also employ features, in which case features are extracted simultaneously with face detection. Feature extraction is also a key to animation and recognition of facial expressions. Face Detection
Moving object segmentation is the important task in video surveillance and conferencing systems. Background subtraction methods for a relatively static background are widely exploited for moving object segmentation and detection in image sequences. However there are several issues like noises, shadows, dynamic background and illumination problem. This paper presents a new approach that combines wavelet approach with the Independent Component Analysis (ICA) for segmenting the moving objects. Our study shows improvements on the above mentioned problems and highly tolerable to changes in indoor and outdoor lighting conditions.
From most video files, blurry lighting has made it a disadvantage to identify facial clarity. There are many attributes leading to the variability of images of a single face that add to the complexity of the recognition problem if they cannot be avoided by careful design of the capture situation. Inadequate constraint or handling of such variability inevitably leads to failures in recognition. These include physical changes: facial expression change; aging; personal appearance such as make-up, glasses, facial hair, hairstyle, and disguise. And imaging changes: lighting variation; camera variations; channel characteristics also in Acquisition geometry changes: change in scale, location and in-plane rotation of the face (facing the camera) as well as rotation in depth (facing the camera obliquely, or presentation of a profile, not full-frontal face).
Delp, provides information about the vehicle which possess abnormal behavior. Anomalous behavior is detected by tracking the vehicle which constantly ignores the lane markings which could be a sign of driver’s negligence. A vehicle with visibly flat tires and moving below the speed limit could be overloaded and pose a threat to human life expectancy. Using two video cameras installed near signals relevant information about the vehicle is extracted using video analysis methods. Traditional Background subtraction method  is commonly used to identify moving vehicle.