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Motivated by the advantages of using shape matching technique in detecting objects in various postures and viewpoints and the discriminative power of local patterns in object recognition, this paper proposes a human detection method combining both shape and appearance cues. In particular, local shapes of the body parts are detected using template matching. Based on body parts' shapes, local appearance...
When we think of an object in a supervised learning setting, we usually perceive it as a collection of fixed attribute values. Although this setting may be suited well for many classification tasks, we propose a new object representation and therewith a new challenge in data mining: an object is no longer described by one set of attributes but is represented in a hierarchy of attribute sets in different...
Feature enhancement in an image is to reinforce some exacted features so that it can be used for object classification and detection. As the thermal image is lack of texture and colorful information, the techniques for visual image feature enhancement is insufficient to apply to thermal images. In this paper, we propose a new gradient-based approach for feature enhancement in thermal image. We use...
Human detection has been widely used in many applications. In the meantime, it is still a difficult problem with many open questions due to challenges caused by various factors such as clothing, posture and etc. By investigating several benchmark methods and frameworks in the literature, this paper proposes a novel method which successfully implements the Real AdaBoost training procedure on multi-scale...
This paper presents a method that combines colour and motion information to track pedestrians in video sequences captured by a fixed camera. Pedestrians are firstly detected using the human detector proposed by Dalal and Triggs which involves computing the histogram of oriented gradients descriptors and classification using a linear support vector machine. For the colour-based model, we extract a...
This paper presents a unified framework for recognizing human action in video using human pose estimation. Due to high variation of human appearance and noisy context background, accurate human pose analysis is hard to achieve and rarely employed for the task of action recognition. In our approach, we take advantage of the current success of human detection and view invariability of local feature-based...
In this paper, we propose a rule-based system for semantically understanding and analyzing the motion of the trajectories of the human activity. The proposed system can be used as a preprocessing phase for enhancing the object detection process. Detected trajectories are classified into three categories; normal, semi-normal and abnormal trajectories according to the distances between their adjacent...
In this paper, we propose an efficient approach to automatic human object segmentation. First, foreground (human object) model and background model are built based on the face detection result, and are used to obtain the seed pixels for foreground and background, respectively. Then seed pixels are clustered using K-means algorithm, and Gaussian mixture models are exploited to generate the foreground/background...
Humans are adept at identifying informative regions in individual images, but it is a slow and often tedious task to identify the salient parts of every image in a large corpus. A machine, on the other hand, can sift through a large amount of data quickly, but machine methods for identifying salient regions are unreliable. In this paper, we develop a new method for identifying salient regions in images...
Human face tracker is one of important research areas that is continuously developing. Many methods have been developed for performing an effective and efficient face tracker based system application. One category of the face tracker methods is the real-time face tracker, which is a challenging task in this field. This paper presents a real-time human face tracker development using facial feature...
A quick estimation of depth is required by artificial vision systems for their self survival and navigation through the environment. Following the selection strategy of biological vision, known as visual attention, can help in accelerating extraction of depth for important and relevant portions of given scenes. Recent studies on depth perception in biological vision indicate that disparity is computed...
In this paper, a new face detection algorithm based on fractal theory and complexion model is proposed. On the basis of the traditional face detection, the new algorithm estimated the fractal dimension of face area to exclude the non-face area and extract the face area fast and accurately. Furthermore, its calculation is simple and it can realize multi-angle detection in the complex background. As...
Human face detection is a key technology in face information processing, the speed of which is very important during real-time face detection for input images or input video sequences. This paper presents a novel face window searching algorithm based on evolutionary agent when detecting faces in gray-scale images. It can quickly And the candidate face windows through the evolutionary computation of...
In this paper, a memory-based Gaussian Mixture Model (MGMM) is proposed inspired by the way human perceives the environment. The human memory mechanism is introduced to model the background, which can make the model remember what the scene has ever been and help the model adapt to the variation of the scene more quickly. Experimental results show the effect of the memory mechanism in segmenting moving...
Today video surveillance systems are widely used in public spaces, such as train stations or airports, to enhance security. In order to observe large and complex facilities a huge amount of cameras is required. These create a massive amount of data to be analyzed. It is therefore crucial to support human security staff with automatic surveillance applications, which will create an alert if security...
Electrostatic detection devices can detect humans by measuring changes in an electrostatic field to indicate the precise location and direction of the subject entity. To analyse the principle of the electrostatic detection system, the mechanisms for generating the human beings' bioelectrical phenomenon are analysed. The human being is regarded as a point charge and constitutes a system with the obstacle...
We present a shape-first approach to finding automobiles and trucks in overhead images and include results from our analysis of an image from the Overhead Imaging Research Dataset [1]. For the OIRDS, our shape-first approach traces candidate vehicle outlines by exploiting knowledge about an overhead image of a vehicle: a vehicle's outline fits into a rectangle, this rectangle is sized to allow vehicles...
Non-rigid characteristics of the human body and the diversification of human articulations are the two challenging problems in pedestrian detection, especially in cluttered scenes that commonly involve multiple people, such as surveillance cameras. Moreover occlusion and body changes also increase the difficulty of the people detecting. The general pictorial structure can divide human body into some...
In this paper, we report on our development of a robotic system that assists people in accomplishing simple tasks in daily life (e.g., retrieving objects for handicapped and elderly people). These tasks, inevitably involve detecting various kinds of objects. In particular, here, we present an interactive method to detect objects using spatial information. Our experimental results confirm the usefulness...
In this paper, we introduce a novel method to detect a carried object seen from a stationary camera using human body silhouette feature information. We use star skeletonization technique with the adaptive centroid point to extract human feature. The carried object is classified using time series of motions of the extracted skeleton limbs. The boundary of the carried object is figured from carried...
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