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Vehicle and Pedestrian Detection is a key problem in computer vision, with several applications including robotics, surveillance and automotive safety. Much of the progress of the past few years has been driven by the availability of challenging public datasets. In this paper, we build up a vehicle and pedestrian detection system by combing Histogram of Oriented Gradients (HoG) feature and support...
In automatic image annotation, it is often extracting low-level visual features from original image for the purpose of mapping to high level image semantic information. In this paper, we propose a novel method which integrates kernel independent component analysis (KICA) and support vector machine (SVM) for analyzing the semantic information of natural images. KICA, which contains a nonlinear kernel...
Detecting objects in images is very important for several application domains in computer vision. This paper presents an experimental study on data transformation of the feature vector in object detection. We use the modified Pyramid of Histograms of Orientation Gradients descriptor and the SVM classifier to form an object detection model. We apply a simple transformation to the histogram features...
This paper presents a vision-based framework for intelligent vehicles to detect and track people riding bicycles in urban traffic environments. To deal with dramatic appearance changes of a bicycle according to different viewpoints as well as nonrigid nature of human appearance, a method is proposed which employs complementary detection and tracking algorithms. In the detection phase, we use multiple...
Detecting the boundaries of objects is a key step in separating foreground objects from the background, which is useful for robotics and computer vision applications, such as object detection, recognition, and tracking. We propose a new method for detecting object boundaries using planar laser scanners (LIDARs) and, optionally, co-registered imagery. We formulate boundary detection as a classification...
A new image based activity recognition method for a person wearing a video camera below the neck is presented in this paper. The wearable device is used to capture video data in front of the wearer. Although the wearer never appears in the video, his or her physical activity is analyzed and recognized using the recorded scene changes resulting from the motion of the wearer. Correspondence features...
In this paper, a vision-based system to detect the eyelid closure for driver alertness monitoring is presented. Similarity measures with three eye templates (open, nearly close, and close) were calculated from many different features, such as 1-D and 2-D histograms and horizontal and vertical projections, of a big set of rectangular eyes images. Two classifiers, Multi-Layer Perceptron and Support...
Object detection and classification have received increased attention recently from computer vision and image processing researchers. Image processing views this problem at a much lower level as compared to machine learning and linear algebraic analysis which focus on the overall statistics of object classes given sufficient data. A good algorithm uses both these approaches to its advantage. It is...
Within the context of a traffic scenario, pedestrians may have several attitudes or perform different actions: wait at the traffic light, cross the street, run for a bus or a taxi, walk or run on the pavement. When performing all these actions, pedestrians have different attitudes: stand, walk, run. We have studied those attitudes and the contexts in which they appear and we have derived some semantic...
This paper presents a head-shoulder detection approach using cascade SVM and histograms of oriented gradients (HOG). The HOG features which are extracted from variable-size blocks can capture salient features of head-shoulder automatically. A two stage cascade using SVM approach is designed to be the classifier. During detection, the majority of negative windows are rejected at the first stage, leaving...
Recent publications and developments based on SVM have shown that using multiple kernels instead of a single one can enhance interpretability of the decision function and improve classifier performance, which motivates researchers to explore the use of homogeneous model obtained as linear combinations of kernels. However, the use of multiple kernels faces the challenge of choosing the kernel weights,...
This paper presents a methodology for analyzing multimodal and multiperspective systems for person surveillance. Using an experimental testbed consisting of two color and two infrared cameras, we can accurately register the color and infrared imagery for any general scene configuration, expanding the scope of multispectral analysis beyond the specialized long-range surveillance experiments of previous...
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