The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Camera tamper detection is the ability to detect faults and operational failures in video surveillance cameras by analyzing the video. Researchers have increasingly focused on such techniques attributing to the ubiquitous deployment of large scale surveillance systems. In this paper, a signal detection theory approach is proposed to quantitatively analyze the information being captured by the camera...
In this paper, we propose a new person re-identification algorithm based on bi-directional superpixel earth mover's distance (BD-SP-EMD). To address the viewpoint change issue, the human body segmentation is first extracted based on background modeling and saliency maps. A bi-directional scheme is then applied to obtain the forward and backward SP-EMD distances. Based on these two distances, pedestrians...
Video-based face recognition (FR) is a challenging task in real-world applications. In still-to-video FR, probe facial regions of interest (ROIs) are typically captured with lower-quality video cameras under unconstrained conditions, where facial appearances vary according to pose, illumination, scale, expression, etc. These video ROIs are typically compared against facial models designed with high-quality...
Faster R-CNN has established itself as the de-facto best object detector but it remains strongly limited in two aspects: (i) it is sensitive to background clutter and its classification performance decreases when it is confronted with more noisy proposals; (ii) it suffers when the objects vary largely in scale and specifically for the small objects. We address both issues with our geometric-proposals...
Re-identification refers to the task of finding the same subject across a network of surveillance cameras. This task must deal with appearance changes caused by variations in illumination, a person's pose, camera viewing angle and background clutter. State-of-the-art approaches usually focus either on feature modeling — designing image descriptors that are robust to changes in imaging conditions,...
Person re-identification is one of the widely studied research topic in the fields of computer vision and pattern recognition. In this paper, we present a deep multi-instance learning approach for person re-identification. Since most publicly available databases for pedestrian re-identification are not enough big, over-fitting problems occur in deep learning architectures. To tackle this problem,...
With the increasing use of unmanned aerial vehicles (UAVs) by consumers, automatic UAV detection systems have become increasingly important for security services. In such a system, video imagery is a core modality for the detection task, because it can cover large areas and is very cost-effective to acquire. Many detection systems consist of two parts: flying object detection and subsequent object...
This paper proposes to examine the possible uses of Artificial Neural Networks (ANN) to aid the landing of an Unmanned Aerial Vehicle (UAV) on a ship. Three distinct phases are proposed. The dataset required for training and testing was produced by simulating a ship's motion at sea using Unity. Phase 1 converts video images from a UAV on-board camera to numeric data. Phase 2 utilizes Phase 1 data...
To quickly and efficiently analyze a large-scale environment by the camera with limited field-of-view, intelligent systems should sequentially select the optimal field-of-view to observe important and informative parts of area. Especially in the image retrieval tasks, small observations could be sequentially selected to improve the performance of image retrieval with less computational costs than...
Human tracking plays an important role in providing activity assistance and care for users in smart homes. This paper presents a method for detecting and tracking of a user in a smart home using multiple sensors. We consider one PTZ camera and multiple Kinects in order to offer continuity over the users movement. Thus, the user can be keep inside the frame for most of the time. In this way, we minimize...
Behavior or human action recognition is one hot research topic in real-time video surveillance system. Dangerous accidents consist of dangerous actions by one or more persons. Thus, action recognition is very important for dangerous accident recognition. If videos captured by public cameras especially dangerous actions related videos can be processed and analyzed immediately to provide an early and...
Indoor scene understanding is central to applications such as robot navigation and human companion assistance. Over the last years, data-driven deep neural networks have outperformed many traditional approaches thanks to their representation learning capabilities. One of the bottlenecks in training for better representations is the amount of available per-pixel ground truth data that is required for...
Non-uniform blind deblurring for general dynamic scenes is a challenging computer vision problem as blurs arise not only from multiple object motions but also from camera shake, scene depth variation. To remove these complicated motion blurs, conventional energy optimization based methods rely on simple assumptions such that blur kernel is partially uniform or locally linear. Moreover, recent machine...
This paper presents a novel large-scale dataset and comprehensive baselines for end-to-end pedestrian detection and person recognition in raw video frames. Our baselines address three issues: the performance of various combinations of detectors and recognizers, mechanisms for pedestrian detection to help improve overall re-identification (re-ID) accuracy and assessing the effectiveness of different...
Supervised deep learning often suffers from the lack of sufficient training data. Specifically in the context of monocular depth map prediction, it is barely possible to determine dense ground truth depth images in realistic dynamic outdoor environments. When using LiDAR sensors, for instance, noise is present in the distance measurements, the calibration between sensors cannot be perfect, and the...
Recent advances have enabled oracle classifiers that can classify across many classes and input distributions with high accuracy without retraining. However, these classifiers are relatively heavyweight, so that applying them to classify video is costly. We show that day-to-day video exhibits highly skewed class distributions over the short term, and that these distributions can be classified by much...
Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric...
Person re-identification is an open and challenging problem in computer vision. Existing approaches have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re-identification problem, where a new camera may...
Motion blur from camera shake is a major problem in videos captured by hand-held devices. Unlike single-image deblurring, video-based approaches can take advantage of the abundant information that exists across neighboring frames. As a result the best performing methods rely on the alignment of nearby frames. However, aligning images is a computationally expensive and fragile procedure, and methods...
Camera relocalisation is an important problem in computer vision, with applications in simultaneous localisation and mapping, virtual/augmented reality and navigation. Common techniques either match the current image against keyframes with known poses coming from a tracker, or establish 2D-to-3D correspondences between keypoints in the current image and points in the scene in order to estimate the...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.