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We present a novel motion descriptor for gesture recognition based on depth camera. Since each object motion leads to a specific depth change characterized by depth difference, we can recognize object motion via Depth Difference Distribution (DDD) in object region. The DDD is approximated by DDD descriptor in three steps. First, each pixel's depth difference value is quantified into Depth Difference...
The hand tremor is one of the most common motion disorders caused by various neurological diseases. Currently diagnostic procedures for tremor evaluation are subjective, and there are no examinations available that can accurately indicate whether tremors are present in a patient's daily life. Early detection of tremor is extremely important for the cure of the disease that causes the tremor. Thus,...
Despite their high stability and compactness, affine moment invariants have received a relatively little attention in action recognition literature. In this paper, we introduce an approach for activity recognition, based on affine moment invariants. In the proposed approach, a compact computationally-efficient shape descriptor is developed by using affine moment invariants. Affine moment invariants...
In this paper we propose a probabilistic method for fusing depth maps in real time for wide-baseline situation. We treat the depth map fusion as a problem of probability density function (pdf) estimation. The original point cloud, instead of the reprojected depth map, is used to estimate the pdf, and a mathematical expectation computation method is proposed to reduce the complexity of the method....
Many emerging application areas in video and image processing require real-time or faster visual concept detection. Examples include indexing of online user-generated video content and 24/7 archiving of TV broadcasts. The current state-of-the-art in concept detection uses bag-of-visual-words features with computationally heavy kernel-based classifiers. We argue that this approach is not feasible for...
We propose a method for estimating 3-D hand postures from 2-D monocular images in real-time. The estimation is based on finding the best matched posture from typical postures whose appearances are learned in advance. For high accuracy, conventional methods require high computational cost for comparing an input with many typical postures. In our method, a tree is automatically generated and trained...
We propose a high-accuracy human detection method featuring a Haar-like filter expressing the human shape and using depth information obtained by capturing people from above with a time-of-flight (TOF) camera. This method extracts object regions by performing background subtraction against this depth information, and passes these extracted object regions through a Haar-like filter based on a human...
For safety purpose, railroad tracks must be inspected regularly for defects or other design non-compliances. One crucial building block in an automatic inspection system is to detect different types of railroad track objects. We introduce a novel global optimization framework to combine evidence from multiple cameras and the distance measuring instrument to improve rail object detection. Our framework...
In this paper, we suggest an interaction model designed to fit users' expectations in front of an image retrieval system. A lightweight relevance feedback strategy, working directly on the 2D projection of image features, allows the user to spatially navigate the media collection maintaining the real-time constraint. A preliminary evaluation of this relevance feedback strategy shows good performance...
In many applications which require real-time keypoint recognition such as Augmented Reality, Random Ferns (RF) is widely used due to its runtime performance. It relies on an offline training phase during which runtime computational burdens are delegated. This leads to robust, accurate, and framerate performance. However, it requires significant amounts of memory, and this has been an obstacle to its...
This paper presents a real-time smoke detection algorithm. A modified Center Symmetric Local Ternary Pattern (CS-LTP) is proposed as the smoke texture descriptor. The change in background texture provides a means to differentiate between smoke and non-smoke region. Combined with the color information of smoke, our method is able to achieve real-time performance at minimum of 30 fps. The comparison...
We present a real-time 3D face identification system using a consumer level depth camera (PrimeSensor). Our system takes a noisy sequence as input and produces reliable identification. Instead of registering a probe to all instances in the database, we propose to only register it with several intermediate references, which considerably reduces processing, while preserving the recognition rate. The...
Matching cells over time has long been the most difficult step in cell tracking. In this paper, we approach this problem by recasting it as a classification problem. We construct a feature set for each cell, and compute a feature difference vector between a cell in the current frame and a cell in a previous frame. Then we determine whether the two cells represent the same cell over time by training...
By segmenting moving objects out and then densely stitching them into background frames, video synopsis provides an efficient way to condense long videos while preserving most activities. Existing video synopsis methods, however, often suffer from either high computation cost due to global energy minimization or unsatisfactory condense rate to avoid loss of important object activities. To address...
We propose a parallelized Annealed Particle Filter method via heterogeneous computing (P-APF), to implement real-time marker-less motion tracking based on OpenCL framework. The overall computing procedure in P-APF is decomposed into several computational tasks with corresponding granularity. According to the degree of parallelism, the tasks are assigned to standard and attached processors respectively,...
One of the most critical limitations of KinectTM-based interfaces is the need for persistence in order to interact with virtual objects. Indeed, a user must keep her arm still for a not-so-short span of time while pointing at an object with which she wishes to interact. The most natural way to overcome this limitation and improve interface reactivity is to employ a vision module able to recognize...
The study of neurological processes and pharmaceutical effects often relies on the analysis of mice behaviour. Automatic tracking tools are widely employed for this purpose, however they are mainly limited to a single mouse. We propose a real time segmentation and tracking algorithm able to manage multiple interacting mice regardless of their fur colour or light settings via an infrared camera. The...
This paper proposes a novel accident prediction approach based on extracting the relation between interested vehicles and increasing risk factor according to anomaly detection in real time traffic videos. In learning process of the traffic model at intersections, we detect all trajectories by tracking of each vehicle and then group them considering road model. All trajectories are clustered by Continuous...
In this paper a combination of an initial disparity estimation using the line-wise hybrid recursive matcher and a subsequent post-processing and up-sampling step using variations of cross-bilateral filtering is presented. The proposed algorithm is realtime capable for image resolutions up to HD and scales well with large disparity ranges. It is specifically designed to allow for a high degree of parallelization...
We propose an approach for pedestrian detection and tracking in low contrast regions. The approach is composed of two modules. Module-1 improves the pixel-based Mixture of Gaussians (MOG) by aggregated background modeling and varying interval differences. Module-2 exploits the Local Patch Variance (LPV) and Partial Silhouette Template (PST) for compensating the incomplete foregrounds often observed...
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