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The objective of this paper is to study the performance of human reidentification based on multi-shot SURF and to assess its degradation according to the angular difference between the test and reference video scene view angles. In this context, we propose a new automatic statistical method of acceptance and rejection of SURF correspondence based on the likelihood ratio of two GMMs learned on the...
We investigate pedestrian detection in depth images. Unlike pedestrian detection in intensity images, pedestrian detection in depth images can reduce the effect of complex background and illumination variation. We propose a new feature descriptor, Histogram of Depth Difference(HDD), for this task. The proposed HDD feature descriptor can describe the depth variance in a local region as Histogram of...
Video analysis aiming at efficient pedestrian detection is an important research area in computer vision and robotics. Although this is a well studied topic, successful detection still remains a challenge in outdoor, low resolution images. We present efficient detection metrics which consider the fact that human movement presents some characteristic patterns. Unlike many methods which perform an intra-blob...
Posture language is rich in ways for individuals to express a variety of desire, feelings and thoughts. Recognizing human posture via computer is a challenging task as it involved multiple issues ranging from image, recognition algorithm and system resources. This proposed work aimed to solve viewpoint variation issue through causal topology design Hidden Markov Model (HMM) for view independent multiple...
This paper is dedicated to people tracking and identification in the multi-camera surveillance system. In the proposed method, each people-image is extracted among each camera and then is labeled with its color vector. Color vector provides a similar probability for each person appeared in different camera¡¦s surveillance frame. By combining the pedestrian¡¦s trajectory with relations among different...
In this paper, a new package-group-transmission-based algorithm is proposed for human activity recognition in videos. The proposed algorithm first models the entire scene as a network where each node in the network corresponds to a segmentation of the scene. Based on this network, we further model people in the scene as groups of packages. Thus, various human activities can be modeled as the process...
Unusual event detection, i.e., identifying unspecified rare/critical events, has become one of the major challenges in visual surveillance. The main solution for this problem is to describe local or global normalness and to report events that do not fit to the estimated models. The majority of existing approaches, however, is limited to a single description (e.g., either appearance or motion) and/or...
Computer vision algorithms for pedestrian detection are often based on classification derived from supervised learning and therefore require training data, which can be built by using generic or specific images. In this field, INRIA datasets are a standard reference but include only few CCTV camera samples. Therefore, for a CCTV camera system it might be interesting to have specific training data...
In this paper, we present a simple yet effective approach to recognizing human activities from video sequences. Our approach integrates the advantages of human action recognition in static images using action key poses and motion based approaches using the variants of Motion History Images (MHI) and Motion Energy Images(MEI). We combine both methodologies to extract a new representation of temporal...
In this paper we introduce video features which are used to predict if people want to exchange contact information with the other in a speed-date, we also use these features to predict how physically attractive participants found their dates. Previous work on predicting and interpreting speed-dates has focused mainly on the audio channel. We use automatically extracted features related to position,...
The use of visual systems to guide robots in manipulation tasks fails when the grasping tool is close to the target, mainly due to the occlusions produced by the robot tool or by the object geometry. Moreover, the robot controller must take into account the presence of human operators within the workspace who can interact in the robot manipulation task. The scheme proposed in this paper solves this...
In this paper we discuss several methods for the creation of 3D models that can provide additional information to robot operators in order to improve their situation awareness of the robot being teleoperated. We derive the 3D models from spatial data gathered from an inexpensive, readily available, video game sensor. In addition, the paper introduces a new method for feature extraction as part of...
Recognizing human activities from common color image sequences faces many challenges, such as complex backgrounds, camera motion, and illumination changes. In this paper, we propose a new 4-dimensional (4D) local spatio-temporal feature that combines both intensity and depth information. The feature detector applies separate filters along the 3D spatial dimensions and the 1D temporal dimension to...
We introduce an action recognition approach based on Partial Least Squares (PLS) and Support Vector Machines (SVM). We extract very high dimensional feature vectors representing spatio-temporal properties of actions and use multiple PLS regressors to find relevant features that distinguish amongst action classes. Finally, we use a multi-class SVM to learn and classify those relevant features. We applied...
We address the discovery of typical activities in video stream contents and its exploitation for estimating the abnormality levels of these streams. Such estimates can be used to select the most interesting cameras to show to a human operator. Our contributions come from the following facets: i) the method is fully unsupervised and learns the activities from long term data; ii) the method is scalable...
This paper proposes pedestrians' attribute analysis such as gender and whether they have bags with them based on multi-layer classification. One of the technically challenging issues is we use only top-view camera images to protect the privacy of the pedestrians. The shape features over the frames are extracted by bag-of-features (BoF) using histogram of oriented gradients (HoG) vectors with the optimized...
In this paper, we describe how information obtained from multiple views using a network of cameras can be effectively combined to yield a reliable and fast human action recognition system. We describe a score-based fusion technique for combining information from multiple cameras that can handle arbitrary orientation of the subject with respect to the cameras. Our fusion technique does not rely on...
Using perception in the context of rendering is a wide spread field. It can be used to speed up calculations or create more detailed images by refining important areas. Saliency, as a perception based method, can identify regions of interest, which should contain more detail. But currently no complete mapping of the 2D operators to an 3D equivalent has been defined. We propose a Bidirectional Saliency...
Compared with traditional video summarization approaches, aerial video summarization is a new and challenging issue for its particular characteristics. Aerial video data is a massive data stream, without pre-edit structures such as sports or news video data, lack of camera motion such as zoom and pan. On account of these characteristics, we proposed a novel approach for summarization. First, we extract...
Tradition gait analysis systems capture the image of a walking subject from either front view or side view. Since the walking direction allowed by the systems is highly restricted, they are inconvenient for long-term evaluation in casual environments (such as home). This study proposes a human gait analysis system with much less restriction on walking direction. In the system, we use the images obtained...
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