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This paper proposes a method to estimate earthquake ground motion by analyzing the video taken with a fixed surveillance camera. In recent years, a network of seismometers was constructed to observe the ground motion. However, the spatial density of the seismometers was not high enough to obtain an adequate spatial distribution of the earthquake ground motion, which varies depending on the local ground...
In this paper, a novel sparse representation based super-resolution (SR) method is proposed to reconstruct a high resolution (HR) face image from a low resolution (LR) observation via training samples. First, a specific LR and HR over-complete dictionary pair is learned for a certain patch over the patches in all training samples with the same position. Second, K-selection mean constrain is used to...
We propose a novel human-in-the-loop surveillance system that continuously learns the properties of objects that are interesting for a human operator. The interesting objects are automatically learned by tracking the eye gaze positions of the operator while he or she monitors the surveillance video. The system automatically detects interesting objects in the surveillance video and forms a new synthetic...
Many accidents at intersection are happend due to judgment errors, which are caused from blind spots of drivers. We propose a method for generating warning messages related to the accidents predicted between blind spots of drivers and vehicle behaviors by using surveillance camera and on-board camera. In this method, vehicle behaviors of straight, right turn, left turn, change to right lane, and change...
A novel two-stage background generation method is proposed. In the first stage, an intensity-level based statistical approach is employed to identify a variety of background variations. No background training is needed. In the second stage, a background variation based heuristic framework is designed to generate a synchronized background video sequence on-line from the surveillance video. This framework...
This paper examines a new problem in large scale stream data: abnormality detection which is localized to a data segmentation process. Unlike traditional abnormality detection methods which typically build one unified model across data stream, we propose that building multiple detection models focused on different coherent sections of the video stream would result in better detection performance....
It is sometimes desired to obscure background of a person on a video conference or foreground people in a surveillance video. Background subtraction (or foreground detection) methods can help separate desired from undesired planes, however current methods often have errors — holes in foreground or background — especially after lighting changes. We describe a unified approach to video privacy that...
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...
Video summarization, which has a tremendous usage area that spreads from information retrieval to data compression, plays a crucial role in the multimedia understanding. In recent years, with the explosion of the number of videos and their area of use, video summarization became a must to signify. Therefore, this work introduces a novel approach for the summarization problem which is based on human...
Object classification is of vital importance to intelligent traffic surveillance. A big challenge is that shooting view changes in different scenes, which leads to sharp accuracy decrease since training and test samples do not follow the same distribution anymore. On the other hand, manual labeling training samples is time and labor consuming. We propose a feature-based transfer learning framework...
In the paper, we present an approach to efficiently summarizing UAV video data. Our approach is based on first detecting and tracking moving objects. Significant camera motion usually present in UAV video data is successfully handled by a robust feature-based frame registration technique. We then devise a saliency-based scoring method to score and rank detected object tracks. Object tracks are then...
This paper presents a novel method for pedestrian counting in surveillance videos, which localizes and tracks the head-shoulders of pedestrians via the integrated bottom-up/top-down processes. In the bottom-up stage, we extract and match informative local image features crossing frames to obtain the initial moving regions (i.e. potential pedestrians). The top-down stage comprises two steps: (i) head-shoulder...
Millions of video surveillance cameras distribute around the world, and capture tremendous number of video data endlessly. Video browsing by frame is time consuming and inefficient, since needless information is abundant in the raw videos. Video synopsis is an effective way to solve this problem by producing a short video abstraction, while keeping the essential activities of the original video. However,...
Representing a video by a set of key frames is useful for efficient video browsing and retrieving. But key frame extraction keeps a challenge in the computer vision field. In this paper, we propose a joint framework to integrate both shot boundary detection and key frame extraction, wherein three probabilistic components are taken into account, i.e. the prior of the key frames, the conditional probability...
With the popularity of vision-based camera surveillance, the research on people localization appeals to much attention. In this paper, we propose an efficient and effective system capable of locating a crowd of dense people in real time, using multiple cameras. For each camera view, sample lines, originated from a vanishing point, of foreground objects are projected on the ground plane. Ground regions...
Recognizing faces in surveillance videos becomes difficult due to the poor quality of the probe data in terms of resolution, noise, blurriness, and varying lighting conditions. In addition, the poses of probe data are usually not frontal view, contrary to the standard format of the gallery data. The discrepancy between the two types of the data makes the existing recognition algorithm less accurate...
Pixel-domain analysis, the mainstream approach to analyze surveillance video, has always been a hot issue in academy and industry. However, with the increasing volume and resolution of surveillance video, the flexibility and efficiency of fast processing is garnering more significance. Under this circumstance, surveillance video analysis in the compressed domain is indeed of strategic importance from...
We propose a novel approach for view-invariant vehicle detection in traffic surveillance videos. Instead of building a monolithic object detector that can model all possible viewpoints, we learn a large array of efficient view-specific models corresponding to different camera views (source domains). When presented with an unseen viewpoint (target domain), closely related models in the source domain...
This paper proposes a new Probabilistic Graphical Model (PGM) to incorporate the scene, event object interaction and the event temporal contexts into Dynamic Bayesian Networks (DBNs) for event recognition in surveillance videos. We first construct the event DBNs for modeling the events from their own appearance and kinematic observations, and then extend the DBN to incorporate the contexts for boosting...
This paper proposes a novel probabilistic approach to utilize clip attributes as hidden knowledge for event recognition. Event recognition in surveillance videos is very challenging due to its large intra-class variations and relative low image resolution. The clip attributes, that are available only during training, provide auxiliary hidden information about the variation of the event appearance...
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