Uncertainty-aware video visual analytics of tracked moving objects

Vast amounts of video data render manual video analysis useless while recent automatic video analytics techniques suffer from insufficient performance. To alleviate these issues we present a scalable and reliable approach exploiting the visual analytics methodology. This involves the user in the iterative process of exploration hypotheses generation and their verification. Scalability is achieved by interactive filter definitions on trajectory features extracted by the automatic computer vision stage. We establish the interface between user and machine adopting the VideoPerpetuoGram (VPG) for visualization and enable users to provide filter-based relevance feedback. Additionally users are supported in deriving hypotheses by context-sensitive statistical graphics. To allow for reliable decision making we gather uncertainties introduced by the computer vision step communicate these information to users through uncertainty visualization and grant fuzzy hypothesis formulation to interact with the machine. Finally we demonstrate the effectiveness of our approach by the video analysis mini challenge which was part of the IEEE Symposium on Visual Analytics Science and Technology 2009

Video surveillance systems-current status and future trends

Within this survey an attempt is made to document the present status of video surveillance systems. The main components of a surveillance system are presented and studied thoroughly. Algorithms for image enhancement, object detection, object tracking, object recognition and item re-identification are presented. The most common modalities utilized by surveillance systems are discussed, putting emphasis on video, in terms of available resolutions and new imaging approaches, like High Dynamic Range video. The most important features and analytics are presented, along with the most common approaches for image / video quality enhancement. Distributed computational infrastructures are discussed (Cloud, Fog and Edge Computing), describing the advantages and disadvantages of each approach. The most important deep learning algorithms are presented, along with the smart analytics that they utilize. Augmented reality and the role it can play to a surveillance system is reported, just before discussing the challenges and the future trends of surveillance

Overview of contextual tracking approaches in information fusion

Proceedings of: Geospatial InfoFusion III. 2-3 May 2013 Baltimore, Maryland, United States.Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of: technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this paper, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion systems design to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.Publicad

Comprehensive Survey and Analysis of Techniques, Advancements, and Challenges in Video-Based Traffic Surveillance Systems

The challenges inherent in video surveillance are compounded by a several factors, like dynamic lighting conditions, the coordination of object matching, diverse environmental scenarios, the tracking of heterogeneous objects, and coping with fluctuations in object poses, occlusions, and motion blur. This research endeavor aims to undertake a rigorous and in-depth analysis of deep learning- oriented models utilized for object identification and tracking. Emphasizing the development of effective model design methodologies, this study intends to furnish a exhaustive and in-depth analysis of object tracking and identification models within the specific domain of video surveillance

KOLAM : human computer interfaces fro visual analytics in big data imagery

In the present day, we are faced with a deluge of disparate and dynamic information from multiple heterogeneous sources. Among these are the big data imagery datasets that are rapidly being generated via mature acquisition methods in the geospatial, surveillance (specifically, Wide Area Motion Imagery or WAMI) and biomedical domains. The need to interactively visualize these imagery datasets by using multiple types of views (as needed) into the data is common to these domains. Furthermore, researchers in each domain have additional needs: users of WAMI datasets also need to interactively track objects of interest using algorithms of their choice, visualize the resulting object trajectories and interactively edit these results as needed. While software tools that fulfill each of these requirements individually are available and well-used at present, there is still a need for tools that can combine the desired aspects of visualization, human computer interaction (HCI), data analysis, data management, and (geo-)spatial and temporal data processing into a single flexible and extensible system. KOLAM is an open, cross-platform, interoperable, scalable and extensible framework for visualization and analysis that we have developed to fulfil the above needs. The novel contributions in this thesis are the following: 1) Spatio-temporal caching for animating both giga-pixel and Full Motion Video (FMV) imagery, 2) Human computer interfaces purposefully designed to accommodate big data visualization, 3) Human-in-the-loop interactive video object tracking - ground-truthing of moving objects in wide area imagery using algorithm assisted human-in-the-loop coupled tracking, 4) Coordinated visualization using stacked layers, side-by-side layers/video sub-windows and embedded imagery, 5) Efficient one-click manual tracking, editing and data management of trajectories, 6) Efficient labeling of image segmentation regions and passing these results to desired modules, 7) Visualization of image processing results generated by non-interactive operators using layers, 8) Extension of interactive imagery and trajectory visualization to multi-monitor wall display environments, 9) Geospatial applications: Providing rapid roam, zoom and hyper-jump spatial operations, interactive blending, colormap and histogram enhancement, spherical projection and terrain maps, 10) Biomedical applications: Visualization and target tracking of cell motility in time-lapse cell imagery, collecting ground-truth from experts on whole-slide imagery (WSI) for developing histopathology analytic algorithms and computer-aided diagnosis for cancer grading, and easy-to-use tissue annotation features.Includes bibliographical reference