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Vision based environmental monitoring using fixed cameras generates large image collections, creating a bottleneck in data analysis. In areas with limited background knowledge of the monitored habitat, this bottleneck can often not be overcome by traditional pattern recognition methods. A new change detection method to identify interesting events such as presence and behavior of different species...
Moving object localization is a popular study in contemporary computer vision, provided the fact that many challenging problems, such as illumination changes, obstacles, object shape transformation, etc, are still to be deeply investigated and properly tackled in moving object localization for the time being. In this study, a new clustering-based strategy is introduced to realize the moving object...
Key frame selection is important to dense 3D reconstruction, especially for unordered image sets. A novel method for key frame selection from unordered image sets is proposed based Distance Depedent Chinese Restaurant Process (DDCRP). First, a bag-of-features word package is constructed to describe each image in a document-like manner, which can be dealt with by the DDCRP model. Second, the overlapping...
Lifelog image clustering is the process of grouping images into events based on image similarities. Until now, groups of images with low variance can be easily clustered, but clustering images with high variance is still a problem. In this paper, we challenge the problem of high variance, and present a methodology to accurately cluster images into their corresponding events. We introduce a new approach...
We introduce the questionable observer detection problem: Given a collection of videos of crowds, determine which individuals appear unusually often across the set of videos. The algorithm proposed here detects these individuals by clustering sequences of face images. To provide robustness to sensor noise, facial expression and resolution variations, blur, and intermittent occlusions, we merge similar...
An improved shots clustering key-frame extraction algorithm based on entropy is presented. Using the color information in the video frames, the algorithm looks every frame of a shot as a special sample and selects appropriate feature. And then through the improvement of the clustering analysis of video sequences to acquire the center value of various classes and the membership degree of every sample...
Many applications in media production need information about moving objects in the scene, e.g. insertion of computer-generated objects, association of sound sources to these objects or visualization of object trajectories in broadcasting. We present a GPU accelerated approach for detecting and tracking salient features in image sequences and we propose an algorithm for clustering the obtained feature...
This paper presents a novel slice-based approach to detect pedestrians in still images. A pedestrian is divided into limited numbers of slice-based sub-regions through a spatio-temporal slice processing. First, sub-regions of interest are detected in different spatio-temporal slice images. Then, a clustering algorithm is proposed to combine these sub-regions into individual pedestrians based on their...
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 order to extract from the video sequence in a complete and consistent moving target, a novel algorithm for video object segmentation based on improved particle swarm optimization (IPSO) is presented. The algorithm fuses brightness segmentation and color information at `region level', as to make up for conventional `pixel level' approaches. The IPSO is taken into account for spatial segmentation...
Safety in railways is mostly achieved by automated operation using a specialized infrastructure. However, many tasks still rely on the decisions and actions of a human crew. Aiming at improving safety in such situations, we present an approach for recognizing railway signals and signs in video sequences taken by an in-vehicle camera. Our approach is based on a model automatically learned from examples,...
In this work we introduce a novel approach for detecting spatiotemporal object-action relations, leading to both, action recognition and object categorization. Semantic scene graphs are extracted from image sequences and used to find the characteristic main graphs of the action sequence via an exact graph-matching technique, thus providing an event table of the action scene, which allows extracting...
Key frames play a very important role in video retrieval. In this paper, we introduce a novel method to extract key frames to represent video shot based on connectivity clustering. Compared with other methods, the proposed method can dynamically divide the frames into clusters depending on the content of shot, and then the frame closest to the cluster centroid is chosen as the key frame for the video...
In this paper, we formulate the feature clustering problem for vehicle detection and tracking as a general MAP problem and solve it using MCMC. The proposed approach exhibits two advantages over existing methods: general Bayesian model can handle arbitrary objective functions and MCMC guarantees global optimal solution. Our algorithm is validated on real-world traffic video sequences, and is shown...
A correct video segmentation, namely the detection of moving objects within a scene plays a very important role in many application in safety, surveillance, traffic monitoring and object detection. The main objective of this paper is to implement an effective background segmentation algorithm for corner sets extracted from video sequences. A dynamic prototype of the structure of background corners...
In this paper we propose a new method for human action categorization by using an effective combination of novel gradient and optic flow descriptors, and creating a more effective codebook modeling the ambiguity of feature assignment in the traditional bag-of-words model. Recent approaches have represented video sequences using a bag of spatio-temporal visual words, following the successful results...
This paper proposes a novel approach for motion primitive segmentation from continuous full body human motion captured on monocular video. The proposed approach does not require a kinematic model of the person, nor any markers on the body. Instead, optical flow computed directly in the image plane is used to estimate the location of segment points. The approach is based on detecting tracking features...
In this paper, a hierarchical video structure summarization approach using Laplacian Eigenmap is proposed, where a small set of reference frames is selected from the video sequence to form a reference subspace to measure the dissimilarity between two arbitrary frames. In the proposed summarization scheme, the shot-level key frames are first detected from the continuity of inter-frame dissimilarity,...
In the problem of face clustering with multi-views, the similarity between faces of different persons with similar pose is usually greater than the similarity between multi-view faces of the same person. This may exert a tremendous impact on the clustering result that sent back to the user. To solve this problem, we should do pose clustering first and then within each dasiapose grouppsila, clustering...
We propose a method for recognizing human actions in videos. Inspired from the recent bag-of-words approaches, we represent actions as documents consisting of words, where a word refers to the pose in a frame. Histogram of oriented gradients (HOG) features are used to describe poses, which are then vector quantized to obtain pose-words. As an alternative to bag-of-words approaches, that only represent...
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