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The growing interest in recent years for gender recognition from face images is mainly attributable to the wide range of possible applications that can be used for commercial and marketing purposes. It is desirable that such algorithms process high resolution video frames acquired by using surveillance cameras in real-time. To the best of our knowledge, however, there are no studies which analyze...
Behavior or human action recognition is one hot research topic in real-time video surveillance system. Dangerous accidents consist of dangerous actions by one or more persons. Thus, action recognition is very important for dangerous accident recognition. If videos captured by public cameras especially dangerous actions related videos can be processed and analyzed immediately to provide an early and...
Vision based human fall action classification from non fall has been given significant importance over the past decade since the rise of falling events related to elderly people living alone has increased. This paper proposes a method to classify falls from non fall action in top Viewed kinect camera depth images. The usage of depth camera images provides an effective solution regarding privacy concerns...
In this paper, we have proposed a method to detect abnormal events for human group activities. Our main contribution is to develop a strategy that learns with very few videos by isolating the action and by using supervised learning. First, we subtract the background of each frame by modeling each pixel as a mixture of Gaussians(MoG) to concatenate the higher order learning only on the foreground....
We propose and analyze a novel framework for tracking a pedestrian in egocentric videos, which is needed for analyzing social gatherings recorded with a wearable camera. The constant camera and pedestrian movement makes this a challenging problem. The main challenges are natural head movement of wearer and target loss and reappearance in a later frame, due to frequent changes in field of view. By...
Activity recognition applications is growing in importance due to two key factors: first there is increased need for more human assistance and surveillance; and second, increased availability of datasets and improved image recognition algorithms have allowed effective recognition of more sophisticated activities. In this paper we develop an activity recognition approach to support visually impaired...
Human action recognition is a way of retrieving videos emerged from Content Based Video Retrieval (CBVR).It is a growing area of research in the field of computer vision nowadays. Human action recognition has gained popularity because of its wide applicability in automatic retrieval of videos of particular action using visual features. The most common stages for action recognition includes: object...
Fine-grained activities are human activities involving small objects and small movements. Automatic recognition of such activities can prove useful for many applications, including detailed diarization of meetings and training sessions, assistive human-computer interaction and robotics interfaces. Existing approaches to fine-grained activity recognition typically leverage the combined use of multiple...
This paper presents a novel object tracking system that combines support vector machines (SVM) and Kalman filter. Objective tracking in videos is a challenging problem due to loss of information, which may be caused by varying illuminance in a scene, occlusions, similar target appearances, and so on. In this paper, we use Kalman filter to predict the dynamics of target object, so as to generate candidate...
A fascinating issue in a digital forensic investigation is that given a digital video, would it be conceivable to recognize the camera model which was utilized to get the video. In this paper we take a simplified form of this issue by attempting to recognize recordings caught by a predetermined number of camera models. We propose various features which could be utilized by a classifier to distinguish...
In this paper, a deep convolutional neural network based approach to the problem of automatically recognizing jersey numbers from soccer videos is presented. Two different jersey number vector encoding schemes are presented and compared to each other. The first treats every number as a separate class, while the second one treats each digit as a class. Additionally, the semi-automatic process for the...
Human action recognition in wild scene is discussed and a novel approach of dense trajectory selection is addressed in this paper, in order to deal with side effects ascribed to clutter, unsteady background interference, camera motion and random noise in the video. First, multi-scale temporal pyramid is constructed from original frames in the video. By employing dense sampling, candidate initial points...
The detection of early manmade fire carries profound meaning in warning systems to prevent fire-related terrorist attacks. Despite a large number of work on fire detection in the computer vision literature, there is no specific method for early stage manmade fire detection, our best knowledge. Compared to traditional fire detection, there is less information on early stage man-made fire for detection,...
The objective of foreground segmentation is to extract the desired foreground object from input videos. Over the years, there have been significant amount of efforts on this topic. Nevertheless, there still lacks a simple yet effective algorithm that can process live videos of objects with fuzzy boundaries (e.g., hair) captured by freely moving cameras. This paper presents an algorithm toward this...
Detecting important regions in videos has been extensively studied for past decades for their wide variety of applications including video summarization and retargeting. Visual attention models draw much attention for this purpose, which find visually salient regions. However, visual attention models ignore intentionally captured regions (ICRs) derived from videographers' intentions, i.e., what the...
Pedestrian detection algorithm for videos taken by cameras amounted on a moving platform is studied. Firstly, Harris operator is used to extract corner points in each frame. Then Normalized Cross-Correlation (NCC) method is used to match those corner points between two adjacent frames. Based on a set of selected matching points, the global motion parameters are calculated using the projection model...
Pedestrian detection systems are valuable in a variety of applications such as in advanced driver assistance systems and advanced robots. This study presents a pedestrian detection system that uses Histogram of Oriented Gradients (HOG) as feature descriptor, and AdaBoost and Linear Support Vector Machines (SVM) as classifiers. The entire system is tested and evaluated in both publicly available databases...
This paper presents a system for vehicle detection, tracking and classification from roadside CCTV. The system counts vehicles and separates them into four categories: car, van, bus and motorcycle (including bicycles). A new background Gaussian Mixture Model (GMM) and shadow removal method have been used to deal with sudden illumination changes and camera vibration. A Kalman filter tracks a vehicle...
In this paper, we propose an approach for human activity categorizing based on the use of optical flow direction and magnitude features. The main contribution of this paper is the feature representation that mirrors the geometry of the human body and relationships between its moving regions when performing activities. The features are quantified using a quantization algorithm. We analyze the performance...
In this paper, we first present the architecture of an intelligent headlight control (IHC) system that we developed in our earlier work. This IHC system aims to automatically control a vehicle's beam state (high beam or low beam) during a night-time drive. A three-level decision framework built around a support vector machine (SVM) learning engine is then briefly discussed. Next, we switch our focus...
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