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A human activity representation method based on event histogram was put forward, and the human activity recognition was realized based on event histogram and KL transform. Ubiquitous sensors were used to acquire data and based on these data human recognition was realized. In order to model the sensor data sequences, the concept of event and event histogram was put forward. The event is corresponding...
Pedestrian detection is an important task in many applications such as intelligent transportation systems, image retrieval, surveillance systems, automated personal assistance, etc. This paper proposes a set of modified Haar-like features that have parallelogram shapes. Using the proposed feature descriptors to develop a rapid detection system for pedestrian detection based on decision tree structure...
We use images that have been collected using an Internet search engine to train color name models for color naming and recognition tasks. Considering color histogram bands as being words of an image and the color names as classes, we use the supervised latent Dirichlet allocation to train our model. To pre-process the training data, we use state-ofthe art salient object detection and a Kullback-Leibler...
This paper presents a novel random forest learning framework to construct a discriminative and informative mid-level feature from low-level features. Since a single low-level feature based representation is not enough to capture the variations of human appearance, multiple low-level features (i.e., optical flow and histogram of gradient 3D features) are fused to further improve recognition performance...
Early Recognition of human activities is a highly desirable functionality for many visual intelligent systems. However, in computer vision, very few work have been devoted to this challenging and interesting task. In this paper, we address human activity early recognition as a pattern recognition problem of time series data. A new model called ARMA-HMM is introduced to integrate both the predictive...
Automatic age classification from human faces is a challenging task which has recently attained an increasing attention. Most of the proposed approaches have however been mainly concerning controlled settings. In this paper, we propose a novel method for age classification in unconstrained conditions and provide extensive performance evaluation on benchmark datasets with standard protocols, thus allowing...
We present a method for human action recognition based on the combination of Histograms of Gradients into orientation tensors. It uses only information from HOG3D: no features or points of interest are extracted. The resulting raw histograms obtained per frame are combined into an orientation tensor, making it a simple, fast to compute and effective global descriptor. The addition of new videos and/or...
Many human actions are correlated, because of compound and/or sequential actions, and similarity. Indeed, human actions are highly correlated in human annotations of 48 actions in the 4,774 videos from visint.org. We exploit such correlations to improve the detection of these 48 human actions, ranging from simple actions such as walk to complex actions such as exchange. We apply a basic pipeline of...
With the development of depth camera technology, it is feasible to get high quality color and depth images synchronously in real time. Thus, RGB-D-based applications are becoming more and more popular, such as pedestrian detection in RGB-D data. As the key point in this application is to search for better descriptions, in this paper we propose a new feature descriptor, Pyramid Depth Self-Similarities...
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...
We describe a method for activity recognition based on distribution of human poses in a video. Pose estimation has shown to be sensitive to the priors given to the inference method; we use a collection of distinctive kinematic tree priors to model the variety of pose variations present in a video. Feature histograms are computed from vector quantized descriptors derived from the pose estimates. A...
This paper introduces a new method for streamed action recognition using Motion Capture (MoCap) data. First, the histograms of action poses, extracted from MoCap data, are computed according to Hausdorf distance. Then, using a dynamic programming algorithm and an incremental histogram computation, our proposed solution recognizes actions in real time from streams of poses. The comparison of histograms...
Interest point detectors (e.g. SIFT, SURF, and MSER) have been successfully applied to numerous applications in high level computer vision tasks such as object detection, and image classification. Despite their popularity, the perceptual relevance of these detectors has not been thoroughly studied. Here, perceptual relevance is meant to define the correlation between these point detectors and free-viewing...
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...
Many genetic disorders or abnormalities that may occur in the future generations can be predicted through analyzing the shape and morphological characteristics of the human chromosomes. This is usually carried out by an expert, inspecting the Karyotype of the patients. A Karyotype is a particular table that presents the chromosome images in a standard format. To generate a Karyotype, it is necessary...
This paper proposes a new human detection method, which is robust to illumination change and does almost not confuse human with other objects even they have similar contours. This method is based on integration with two features: Higher-order Local Auto-Correlation (HLAC) features and Histograms of Oriented Gradients (HOG) features. HLAC features can give a broad pattern of gray scale image. The features...
Human tracking is an important function to an automatic surveillance system using a vision sensor. Human face is one of the most significant features to detect person(s) in an image. However, face is not always observed from a single camera. Therefore, it is difficult to identify a person exactly in an image due to the variety of poses. This paper describes a method for automatic human tracking based...
A new approach for quantizing feature vectors of interest points is proposed in this study. The method utilizes the histograms which work as a dimensionality reduction algorithm for quantizing the local and global features. The performance of activity recognition is generally depend upon the quantity of significant features but with proper feature quantization one can delivered the same performance...
In this paper, deep pipelined FPGA implementation of a real-time image-based human detection algorithm is presented. By using binary patterned HOG features, AdaBoost classifiers generated by offline training, and some approximation arithmetic strategies, our architecture can be efficiently fitted on a low-end FPGA without any external memory modules. Empirical evaluation reveals that our system achieves...
This paper introduces a novel face recognition method based on Adaptive Patch Alignment Based Local Binary Patterns (APALBP). LBP is one of the most effective features to face recognition. However, the effectiveness of this feature greatly relies on face alignment, i.e., since LBP is in fact an image feature rather than face feature, pose difference will directly influence the recognition performance...
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