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Manifold learning has been effectively used in computer vision applications for dimensionality reduction that improves classification performance and reduces computational load. Grassmann manifolds are well suited for computer vision problems because they promote smooth surfaces where points are represented as subspaces. In this paper we propose Grassmannian Sparse Representations (GSR), a novel subspace...
In this paper, we propose a novel approach to recognize dynamic hand gestures from depth video by integrating Edge Enhanced Depth Motion Map together with Histogram of Gradient descriptor. The novelty of this paper has two aspects: first, we propose a novel feature representation, Edge Enhanced Depth Motion Map (E2DMM), balancing the information weighing between shape and motion, which is more suitable...
One of the dominant approaches to gesture recognition, especially when we have one or few samples per class, is to compute the time-warped distance between the two sequences and perform nearest-neighbor classification. In this work, we show that we get much better results if instead we consider the similarity of the pattern of frame-wise distances of these two sequences with a third (anchor) sequence...
In this paper, we propose a novel shape-theoretic framework for dynamical analysis of human movement from 3D data. The key idea we propose is the use of global descriptors of the shape of the dynamical attractor as a feature for modeling actions. We apply this approach to the novel application scenario of estimation of movement quality from a single-marker for future usage in home-based stroke rehabilitation...
We address the problem of automated quantitative evaluation of musculo-skeletal disorders using a 3D sensor. This enables a non-invasive home monitoring system which extracts and analyzes the subject's motion symptoms and provides clinical feedback. The subject is asked to perform several clinically validated standardized tests (e.g. sit-to-stand, repeated several times) in front of a 3D sensor to...
The paper presents a method for human detection and tracking in depth images captured by a top-view camera system. We introduce a new feature descriptor which outperforms state-of-the-art features like Simplified Local Ternary Patterns in the given scenario. We use this feature descriptor to train a head-shoulder detector using a discriminative class scheme. A separate processing step ensures that...
In this paper, a new method of human detection based on depth map from 3D sensor Kinect is proposed. First, the pixel filtering and context filtering are employed to roughly repair defects on the depth map due to information inaccuracy captured by Kinect. Second, a dataset consisting of depth maps with various indoor human poses is constructed as benchmark. Finally, by introducing Kirsch mask and...
In this paper we present an algorithm for estimating 3D pose of human targets using multiple, synchronized video streams obtained from a set of calibrated visual sensors. Our method uses 3D visual hull, reconstructed from multiview image silhouettes, to estimate skeleton and 3D pose of the human target. The key contribution of this work is to extend predictive human pose estimation algorithms used...
There has been an enormous increase of 3D human motion data in various fields, such as 3D gaming (such an EA sports) and medical fields (physical medicine and rehabilitations). We need an effective content-based 3D human motion retrieval scheme supporting human-level language queries. However, there is a big semantic gap between these two media since the 3D Human motion data and text are heterogeneous...
In this paper, we propose a method for compensating for motion features that are outside a given viewing angle by using a regression estimate that is based on a correlation between the motion features from human bodies deficient visually, when recognizing the actions of people whose bodies are only partially within the given view. This compensation is good for use in situations where parts of a person's...
We propose hinge-loss Markov random fields (HLMRFs), a powerful class of continuous-valued graphical models, for high-level computer vision tasks. HL-MRFs are characterized by log-concave density functions, and are able to perform efficient, exact inference. Their templated hinge-loss potential functions naturally encode soft-valued logical rules. Using the declarative modeling language probabilistic...
We describe a general and exact method to speed up the training of linear object detection systems operating in a sliding, multi-scale window fashion, such as deformable part-based models. Our approach consists of reformulating the computation of the gradient as a convolution, and making use of properties of the Fourier transform to obtain a speedup factor proportional to the linear filters' sizes...
Objects such as pedestrians exhibit large intra-class variations, posing significant challenges for visual object detection. State-of-the-art part-based models explicitly model object deformations, but are limited in their ability to handle image variations incurred by other geometric and photometric changes, such as human pose, lighting, occlusions, and large appearance variations. In this paper,...
The fields of 3D computer vision, 3D robotic perception and photogrammetry rely more and more heavily on matching 3D local descriptors, computed on a small neighborhood of a point cloud or a mesh, to carry out tasks such as point cloud registration, 3D object recognition and pose estimation in clutter, SLAM, 3D object retrieval. One major drawback of these applications is currently the high computational...
A typical digital signal processor (DSP) uses hierarchical memory to handle the trade-off between cost and speed. It has a fast on-chip memory with data-access rates similar to the DSP's processing rate but it is not large enough to hold the entire Image data. Image buffers typically reside in the larger external memory like DDR whose data access rate is ~4-6X slower than the processor rate. Cache...
This paper describes an architecture framework using heterogeneous hardware accelerators for embedded vision applications. This approach leverages the recent single-chip heterogeneous FPGAs that combine powerful multicore processors with extensive programmable gate array fabric on the same die. We present a framework using an extensive library of pipelined real time vision hardware accelerators and...
Lane feature extraction is one of the key computational steps in lane analysis systems. In this paper, we propose a lane feature extraction method, which enables different configurations of embedded solutions that address both accuracy and embedded systems' constraints. The proposed lane feature extraction process is evaluated in detail using real world lane data, to explore its effectiveness for...
New SOC like the Xilinx Zynq 7045 allow researchers and developers to combine the advantages of writing software for control functionality and having accelerators in the FPGA logic for the number crunching. The dual core Cortex-A9 ARM processor runs with up to 1 GHz and the FPGA has up to 900 DSP slices allowing a performance of up to 1, 334 GMACs. SCS is porting a lot of algorithms like SGM stereo,...
We present a GPU-accelerated, real-time and practical, pedestrian detection system, which efficiently computes pedestrian-specific shape and motion cues and combines them in a probabilistic manner to infer the location and occlusion status of pedestrians viewed by a stationary camera. The articulated pedestrian shape is approximated by a mean contour template, where template matching against an incoming...
Object detection, and in particular pedestrian detection, is a challenging task, due to the wide variety of appearances. The application domain is extremely broad, ranging from e.g. surveillance to automotive safety systems. Many practical applications however often rely on stringent real-time processing speeds combined with high accuracy needs. These demands are contradictory, and usually a compromise...
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