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Full-Text Articles in Engineering
Human Detection, Tracking And Segmentation In Surveillance Video, Guang Shu
Human Detection, Tracking And Segmentation In Surveillance Video, Guang Shu
Electronic Theses and Dissertations
This dissertation addresses the problem of human detection and tracking in surveillance videos. Even though this is a well-explored topic, many challenges remain when confronted with data from real world situations. These challenges include appearance variation, illumination changes, camera motion, cluttered scenes and occlusion. In this dissertation several novel methods for improving on the current state of human detection and tracking based on learning scene-specific information in video feeds are proposed. Firstly, we propose a novel method for human detection which employs unsupervised learning and superpixel segmentation. The performance of generic human detectors is usually degraded in unconstrained video environments …
Visual Geo-Localization And Location-Aware Image Understanding, Amir Roshan Zamir
Visual Geo-Localization And Location-Aware Image Understanding, Amir Roshan Zamir
Electronic Theses and Dissertations
Geo-localization is the problem of discovering the location where an image or video was captured. Recently, large scale geo-localization methods which are devised for ground-level imagery and employ techniques similar to image matching have attracted much interest. In these methods, given a reference dataset composed of geo-tagged images, the problem is to estimate the geo-location of a query by finding its matching reference images. In this dissertation, we address three questions central to geo-spatial analysis of ground-level imagery: 1) How to geo-localize images and videos captured at unknown locations? 2) How to refine the geo-location of already geo-tagged data? 3) …
Holistic Representations For Activities And Crowd Behaviors, Berkan Solmaz
Holistic Representations For Activities And Crowd Behaviors, Berkan Solmaz
Electronic Theses and Dissertations
In this dissertation, we address the problem of analyzing the activities of people in a variety of scenarios, this is commonly encountered in vision applications. The overarching goal is to devise new representations for the activities, in settings where individuals or a number of people may take a part in specific activities. Different types of activities can be performed by either an individual at the fine level or by several people constituting a crowd at the coarse level. We take into account the domain specific information for modeling these activities. The summary of the proposed solutions is presented in the …
Human Action Localization And Recognition In Unconstrained Videos, Hakan Boyraz
Human Action Localization And Recognition In Unconstrained Videos, Hakan Boyraz
Electronic Theses and Dissertations
As imaging systems become ubiquitous, the ability to recognize human actions is becoming increasingly important. Just as in the object detection and recognition literature, action recognition can be roughly divided into classification tasks, where the goal is to classify a video according to the action depicted in the video, and detection tasks, where the goal is to detect and localize a human performing a particular action. A growing literature is demonstrating the benefits of localizing discriminative sub-regions of images and videos when performing recognition tasks. In this thesis, we address the action detection and recognition problems. Action detection in video …
A Study Of Localization And Latency Reduction For Action Recognition, Syed Zain Masood
A Study Of Localization And Latency Reduction For Action Recognition, Syed Zain Masood
Electronic Theses and Dissertations
The success of recognizing periodic actions in single-person-simple-background datasets, such as Weizmann and KTH, has created a need for more complex datasets to push the performance of action recognition systems. In this work, we create a new synthetic action dataset and use it to highlight weaknesses in current recognition systems. Experiments show that introducing background complexity to action video sequences causes a significant degradation in recognition performance. Moreover, this degradation cannot be fixed by fine-tuning system parameters or by selecting better feature points. Instead, we show that the problem lies in the spatio-temporal cuboid volume extracted from the interest point …
Gradient Based Mrf Learning For Image Restoration And Segmentation, Kegan Samuel
Gradient Based Mrf Learning For Image Restoration And Segmentation, Kegan Samuel
Electronic Theses and Dissertations
The undirected graphical model or Markov Random Field (MRF) is one of the more popular models used in computer vision and is the type of model with which this work is concerned. Models based on these methods have proven to be particularly useful in low-level vision systems and have led to state-of-the-art results for MRF-based systems. The research presented will describe a new discriminative training algorithm and its implementation. The MRF model will be trained by optimizing its parameters so that the minimum energy solution of the model is as similar as possible to the ground-truth. While previous work has …
Patterns Of Motion: Discovery And Generalized Representation, Imran Saleemi
Patterns Of Motion: Discovery And Generalized Representation, Imran Saleemi
Electronic Theses and Dissertations
In this dissertation, we address the problem of discovery and representation of motion patterns in a variety of scenarios, commonly encountered in vision applications. The overarching goal is to devise a generic representation, that captures any kind of object motion observable in video sequences. Such motion is a significant source of information typically employed for diverse applications such as tracking, anomaly detection, and action and event recognition. We present statistical frameworks for representation of motion characteristics of objects, learned from tracks or optical flow, for static as well as moving cameras, and propose algorithms for their application to a variety …
Feature Pruning For Action Recognition In Complex Environment, Adarsh Nagaraja
Feature Pruning For Action Recognition In Complex Environment, Adarsh Nagaraja
Electronic Theses and Dissertations
A significant number of action recognition research efforts use spatio-temporal interest point detectors for feature extraction. Although the extracted features provide useful information for recognizing actions, a significant number of them contain irrelevant motion and background clutter. In many cases, the extracted features are included as is in the classification pipeline, and sophisticated noise removal techniques are subsequently used to alleviate their effect on classification. We introduce a new action database, created from the Weizmann database, that reveals a significant weakness in systems based on popular cuboid descriptors. Experiments show that introducing complex backgrounds, stationary or dynamic, into the video …
Towards Calibration Of Optical Flow Of Crowd Videos Using Observed Trajectories, Iman K. Elbadramany
Towards Calibration Of Optical Flow Of Crowd Videos Using Observed Trajectories, Iman K. Elbadramany
Electronic Theses and Dissertations
The need exists for finding a quantitative method for validating crowd simulations. One approach is to use optical flow of videos of real crowds to obtain velocities that can be used for comparison to simulations. Optical flow, in turn, needs to be calibrated to be useful. It is essential to show that optical flow velocities obtained from crowd videos can be mapped into the spatially averaged velocities of the observed trajectories of crowd members, and to quantify the extent of the correlation of the results. This research investigates methods to uncover the best conditions for a good correlation between optical …
Markerless Tracking Using Polar Correlation Of Camera Optical Flow, Prince Gupta
Markerless Tracking Using Polar Correlation Of Camera Optical Flow, Prince Gupta
Electronic Theses and Dissertations
We present a novel, real-time, markerless vision-based tracking system, employing a rigid orthogonal configuration of two pairs of opposing cameras. Our system uses optical flow over sparse features to overcome the limitation of vision-based systems that require markers or a pre-loaded model of the physical environment. We show how opposing cameras enable cancellation of common components of optical flow leading to an efficient tracking algorithm that captures five degrees of freedom including direction of translation and angular velocity. Experiments comparing our device with an electromagnetic tracker show that its average tracking accuracy is 80% over 185 frames, and it is …
Spatio-Temporal Maximum Average Correlation Height Templates In Action Recognition And Video Summarization, Mikel Rodriguez
Spatio-Temporal Maximum Average Correlation Height Templates In Action Recognition And Video Summarization, Mikel Rodriguez
Electronic Theses and Dissertations
Action recognition represents one of the most difficult problems in computer vision given that it embodies the combination of several uncertain attributes, such as the subtle variability associated with individual human behavior and the challenges that come with viewpoint variations, scale changes and different temporal extents. Nevertheless, action recognition solutions are critical in a great number of domains, such video surveillance, assisted living environments, video search, interfaces, and virtual reality. In this dissertation, we investigate template-based action recognition algorithms that can incorporate the information contained in a set of training examples, and we explore how these algorithms perform in action …
Multi-View Approaches To Tracking, 3d Reconstruction And Object Class Detection, Saad Khan
Multi-View Approaches To Tracking, 3d Reconstruction And Object Class Detection, Saad Khan
Electronic Theses and Dissertations
Multi-camera systems are becoming ubiquitous and have found application in a variety of domains including surveillance, immersive visualization, sports entertainment and movie special effects amongst others. From a computer vision perspective, the challenging task is how to most efficiently fuse information from multiple views in the absence of detailed calibration information and a minimum of human intervention. This thesis presents a new approach to fuse foreground likelihood information from multiple views onto a reference view without explicit processing in 3D space, thereby circumventing the need for complete calibration. Our approach uses a homographic occupancy constraint (HOC), which states that if …
Detecting Curved Objects Against Cluttered Backgrounds, Jan Prokaj
Detecting Curved Objects Against Cluttered Backgrounds, Jan Prokaj
Electronic Theses and Dissertations
Detecting curved objects against cluttered backgrounds is a hard problem in computer vision. We present new low-level and mid-level features to function in these environments. The low-level features are fast to compute, because they employ an integral image approach, which makes them especially useful in real-time applications. The mid-level features are built from low-level features, and are optimized for curved object detection. The usefulness of these features is tested by designing an object detection algorithm using these features. Object detection is accomplished by transforming the mid-level features into weak classifiers, which then produce a strong classifier using AdaBoost. The resulting …
Towards A Self-Calibrating Video Camera Network For Content Analysis And Forensics, Imran Junejo
Towards A Self-Calibrating Video Camera Network For Content Analysis And Forensics, Imran Junejo
Electronic Theses and Dissertations
Due to growing security concerns, video surveillance and monitoring has received an immense attention from both federal agencies and private firms. The main concern is that a single camera, even if allowed to rotate or translate, is not sufficient to cover a large area for video surveillance. A more general solution with wide range of applications is to allow the deployed cameras to have a non-overlapping field of view (FoV) and to, if possible, allow these cameras to move freely in 3D space. This thesis addresses the issue of how cameras in such a network can be calibrated and how …
Depth From Defocused Motion, Zarina Myles
Depth From Defocused Motion, Zarina Myles
Electronic Theses and Dissertations
Motion in depth and/or zooming causes defocus blur. This work presents a solution to the problem of using defocus blur and optical flow information to compute depth at points that defocus when they move. We first formulate a novel algorithm which recovers defocus blur and affine parameters simultaneously. Next we formulate a novel relationship (the blur-depth relationship) between defocus blur, relative object depth and three parameters based on camera motion and intrinsic camera parameters. We can handle the situation where a single image has points which have defocused, got sharper or are focally unperturbed. Moreover, our formulation is valid regardless …