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Detecting pedestrians in images is a challenging task, especially for the intelligent vehicle environment where there is a real-time requirement which limits the computational complexity of algorithms. In this paper, we demonstrate a near real-time and robust pedestrian detection system in the context of intelligent vehicle. This is achieved by integrating Locally Assembled Binary (LAB) features with...
Even the tiniest fly has faster analysis and processing capabilities than most of our complex calculating devices. Real time processing and recognition capability by the human brain of both still and moving images is well beyond any known artificial computer based system. Our research team tried to explore some still and moving image processing techniques that are better suited to be used by biological...
A foreground extraction algorithm based on back-ground subtraction and edge detection is proposed to obtain the foreground with a little change. An action classification method based on Motion Energy Image (MEI) and Locality Preserving Projections (LPP) is proposed. The high dimensional feature space is non-linearly reduced to lower dimensional space, which outperforms PCA and 2DPCA. The nearest-neighbor...
Human detection and recognition at a distance is recently a matter of great concern among computer vision researchers. This paper introduces a new set of human body features for the recognition of detected human as an object. The feature extraction is performed by an established human model consisting of five parts. These features consist of geometric calculations of detected object and their different...
Recently, the studies of human motion analysis have attracted great attention among the researches in the field of biomechanics, medicine and sports by analyzing the joints, postures, and movements of the human. Our research focuses on analyzing the joints movement of a professional golfer. We represent those joint movements with an articulate stick human model. This paper presents a method for tracking...
In this paper a novel algorithm for human action recognition is presented. This approach is based on Two-Dimensional Principal Component Analysis (2DPCA) and Vector Quantization (VQ) in the spatial-temporal domain. This method reduces computational complexity by a factor of 98, while maintaining the storage requirement and the recognition accuracy, compared with some of the most recent approaches...
As robot assisted living is gaining more attentions for elderly care recently, automated human daily activity recognition becomes more important in human-robot interaction. In this paper, we proposed an approach to indoor human daily activity recognition which combines motion data and location information. One inertial sensor is worn on the right thigh of a human subject to collect motion data, while...
A foreground extraction algorithm based on background subtraction and edge detection was proposed to obtain the foreground with a little change. An action classification method based on Enhanced Gait Energy Image (EGEI) and Locality Preserving Projections (LPP) was used. The high dimensional feature space was non-linearly reduced to lower dimensional space, which outperformed PCA and 2DPCA. The nearest-neighbor...
This paper proposes an annealed particle swarm optimization based particle filter algorithm for articulated 3D human body tracking. In our algorithm, a sampling covariance and an annealing factor are incorporated into the velocity updating equation of particle swarm optimization (PSO). The sampling covariance and the annealing factor are initiated with appropriate values at the beginning of the PSO...
This paper presents a novel control approach based on digital image processing for elevator door protection system. This system is a non-contact type of elevator door protection system, which incorporates the background difference method and inter-frame difference method to achieve moving object detection and identifies the human body by the ratio of the image area and domain of variation. Finally,...
Motion-based face recognition is a young research topic, inspired mainly by psychological studies on motion-based perception of human faces. Unlike its close relative, appearance-based face recognition, motion-based face recognition extracts personal characteristics from facial motion (e.g. smile) and uses the information to recognize human identity. However, existing studies in this field are limited...
We investigate dynamical models of human motion that can support both synthesis and analysis tasks. Unlike coarser discriminative models that work well when action classes are nicely separated, we seek models that have fine-scale representational power and can therefore model subtle differences in the way an action is performed. To this end, we model an observed action as an (unknown) linear time-invariant...
Gait recognition has recently attracted increasing interest from the biometric society. In this paper, we present a gait recognition system based on the fusion of multiple gait cycles using a new gait representation. First, a gait sequence is automatically partitioned into multiple gait cycles by finding the local minima of width signal. After gait cycle partitioning, we extract a new gait feature...
While the problem of tracking 3D human motion has been widely studied, most approaches have assumed that the person is isolated and not interacting with the environment. Environmental constraints, however, can greatly constrain and simplify the tracking problem. The most studied constraints involve gravity and contact with the ground plane. We go further to consider interaction with objects in the...
The determination of ethnicity of an individual, as a soft biometrics, can be very useful in a video-based surveillance system. Currently, face is commonly used to determine the ethnicity of a person. Up to now, gait has been used for individual recognition and gender classification but not for ethnicity determination. This paper focuses on the ethnicity determination based on fusion of multi-view...
A gesture is a form of non-verbal communication in which visible bodily actions communicate particular messages, either in place of speech or together and in parallel with spoken words. Gestures are important in the communication between human and human. It will make a robot more human-friendly to enable it to communicate with human by gestures. Our research addresses to develop a method to recognize...
The paper deals with the issue of action recognition as an application of the new 3D time-of-flight (ToF) camera, exploiting the special ability of the device to measure distances. Segmentation of moving people is straightforward from the distance information and subsequent steps of the processing chain follow in a classical way. We describe the first results on action recognition using ToF camera...
In this work, we present an approach to fuse video with orientation data obtained from extended inertial sensors to improve and stabilize full-body human motion capture. Even though video data is a strong cue for motion analysis, tracking artifacts occur frequently due to ambiguities in the images, rapid motions, occlusions or noise. As a complementary data source, inertial sensors allow for drift-free...
We introduce a new class of probabilistic latent variable model called the Implicit Mixture of Conditional Restricted Boltzmann Machines (imCRBM) for use in human pose tracking. Key properties of the imCRBM are as follows: (1) learning is linear in the number of training exemplars so it can be learned from large datasets; (2) it learns coherent models of multiple activities; (3) it automatically discovers...
We present an automatic and efficient method to extract spatio-temporal human volumes from video, which combines top-down model-based and bottom-up appearance-based approaches. From the top-down perspective, our algorithm applies shape priors probabilistically to candidate image regions obtained by pedestrian detection, and provides accurate estimates of the human body areas which serve as important...
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