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In this paper, we propose a spatiotemporal salient objects-based approach for video retrieval. The spatiotemporal salient object is defined as the region sequence which is spatial salient and temporal continuous at the same time. As attention analysis is an effective mechanism for salient information selection, it provides a practical approach to narrow the semantic gap. Most existing methods extract...
Aiming at the demand of mining target from a mass of battlefield video, a mining method based on key frames is discussed. Firstly, the key frames are extracted from battlefield video through content-based video retrieval process. Then, target recognition of key frames is done to mine the target message. The practical calculation shows the mining method is feasible.
This paper presents a novel method for simultaneous pedestrian detection and tracking in image sequences. The motion detection and tracking are addressed in a common framework that employs a geometric active contour model, in which an evolving curve was formulated by the level set method. The pedestrian detection module is started after the curve evolution, which stops the propagating contour on the...
Detection and classification of traffic signs is one of the most studied Advanced Driver Assistance Systems (ADAS) and some solutions are already installed in vehicles. Nevertheless these systems still have room for improvement in terms of speed and performance. When driving at high speed, warning systems require very fast processing of the video stream in order to lose as few frames as possible and...
The paper proposed a fast face detection algorithm which integrates differential algorithm and mosaic rules for videos. The comprehensive algorithm with temporal difference and background difference was used for acquiring and tracking the moving objects, which can accurately locate the target region. In the region, the gray rules based on mosaic image were used for detecting face, in which the integral...
This paper proposes a novel human action recognition approach which represents each video sequence by a cumulative skeletonized images (called CSI) in one action cycle. Normalized-polar histogram corresponding to each CSI is computed. That is the number of pixels in CSI which is located in the certain distance and angles of the normalized circle. Using hierarchical classification in two levels, human...
This study introduces a novel classification algorithm for learning and matching sequences in view independent object tracking. The proposed learning method uses adaptive boosting and classification trees on a wide collection (shape, pose, color, texture, etc.) of image features that constitute a model for tracked objects. The temporal dimension is taken into account by using k-mean clusters of sequence...
In this paper, we explore the idea of using only pose, without utilizing any temporal information, for human action recognition. In contrast to the other studies using complex action representations, we propose a simple method, which relies on extracting “key poses” from action sequences. Our contribution is two-fold. Firstly, representing the pose in a frame as a collection of line-pairs, we propose...
Facial Expression Recognition has mostly been done on frontal or near frontal faces. However, most of the faces in real life are non-frontal. This paper deals with in-plane rotation of faces in image sequences and considers the six universal facial expressions. The proposed approach does not need to rotate the image to frontal position. FER by rotating images to frontal is sensitive to determination...
We present an efficient technique based on histogram evolution for summarizing video sequences to make them more amenable to browsing and retrieval. First, a ground-truth database of videos is generated in which the shot breaks are detected by human subjects and numbered in order. Three types of histogram are then used to capture the characteristics of color content containing in the video frames...
Assessing the usability of objects for a specific population can be laborious and time consuming. Furthermore, for the older adult population, the usability of objects involved in the completion of tasks of daily living is critical to `aging-in-place' and the preservation of independence. This paper explores the automation of the process of observing older adults with Alzheimer's as they use various...
Intestinal motility analysis is an important examination in detection of various intestinal malfunctions. One of the big challenges of automatic motility analysis is how to compare sequence of images and extract dynamic paterns taking into account the high deformability of the intestine wall as well as the capsule motion. From clinical point of view the ability to align endoluminal scene sequences...
Despite impressive progress in people detection the performance on challenging datasets like Caltech Pedestrians or TUD-Brussels is still unsatisfactory. In this work we show that motion features derived from optic flow yield substantial improvements on image sequences, if implemented correctly - even in the case of low-quality video and consequently degraded flow fields. Furthermore, we introduce...
A supervised softbot utilized for analyzing, segmenting, and properly classifying video clips pertaining to a wide variety of sporting events is presented. First, selected action scenes (i.e., training sequences) of a given sporting event are automatically segmented into real-world objects representing the participants of the activity. These objects correspond to the players, the playing field (or...
Although widely used in practice for real time ability, the mean shift algorithm was weak at target model description and was robust for infrared image sequences form motion platform. In order to enhance the adaptability and target description ability of the mean shift algorithm, meanwhile to make up the shortcomings of the nuclear density estimation based on gradation feature, an adaptive Kalman-mean...
The model of visual attention infrared target detection algorithm is presented. Mainly the visual features are extracted from the brightness contrast and movement in the current frame still images and image sequences of the motion vector, and then a linear convergence significantly diagram, with locally adaptive thresholding instead of "Winner-Takes-All" neural network (Winner-Take-All,...
This paper presents an adaptive tracking algorithm by online features enhancement. To avoid the distraction of the similar background on tracker, Bayes decision rule is applied to calculate the posterior probability of every pixel belonging to the object and generate a set of candidate confidence maps according to the conditional sample densities from object and background on different features. We...
In this paper, a new action feature descriptor PEM (PCRM-EOH-MOH) is proposed for fast human action recognition. This descriptor is constructed based on three information channels: Pixel Change Ratio Map (PCRM), Edge Orientation Histogram (EOH) and Motion Orientation Histogram (MOH) features. A video sequence is first represented as a collection of PEM features. Then, video representations are constructed...
We propose a novel method for removing irrelevant frames from a video given user-provided frame-level labeling for a very small number of frames. We first hypothesize a number of windows which possibly contain the object of interest, and then determine which window(s) truly contain the object of interest. Our method enjoys several favorable properties. First, compared to approaches where a single...
We propose to model a tracked object in a video sequence by locating a list of object features that are ranked according to their ability to differentiate against the image background. The Bayesian inference is utilised to derive the probabilistic location of the object in the current frame, with the prior being approximated from the pervious frame and the posterior achieved via the current pixel...
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