A distributed camera system for multi-resolution surveillance

We describe an architecture for a multi-camera, multi-resolution surveillance system. The aim is to support a set of distributed static and pan-tilt-zoom (PTZ) cameras and visual tracking algorithms, together with a central supervisor unit. Each camera (and possibly pan-tilt device) has a dedicated process and processor. Asynchronous interprocess communications and archiving of data are achieved in a simple and effective way via a central repository, implemented using an SQL database. Visual tracking data from static views are stored dynamically into tables in the database via client calls to the SQL server. A supervisor process running on the SQL server determines if active zoom cameras should be dispatched to observe a particular target, and this message is effected via writing demands into another database table. We show results from a real implementation of the system comprising one static camera overviewing the environment under consideration and a PTZ camera operating under closed-loop velocity control, which uses a fast and robust level-set-based region tracker. Experiments demonstrate the effectiveness of our approach and its feasibility to multi-camera systems for intelligent surveillance

Active visual tracking in multi-agent scenarios

PhD thesisCamera-equipped robots (agents) can autonomously follow people to provide continuous assistance in wide areas, e.g. museums and airports. Each agent serves one person (target) at a time and aims to maintain its target centred on the camera’s image plane with a certain size (active visual tracking) without colliding with other agents and targets in its proximity. It is essential that each agent accurately estimates the state of itself and that of nearby targets and agents over time (i.e. tracking) to perform collision-free active visual tracking. Agents can track themselves with either on-board sensors (e.g. cameras or inertial sensors) or external tracking systems (e.g. multi-camera systems). However, on-board sensing alone is not sufficient for tracking nearby targets due to occlusions in crowded scenes, where an external multi-camera system can help. To address scalability of wide-area applications and accurate tracking, this thesis proposes a novel collaborative framework where agents track nearby targets jointly with wireless ceiling-mounted static cameras in a distributed manner. Distributed tracking enables each agent to achieve agreed state estimates of targets via iteratively communicating with neighbouring static cameras. However, such iterative neighbourhood communication may cause poor communication quality (i.e. packet loss/error) due to limited bandwidth, which worsens tracking accuracy. This thesis proposes the formation of coalitions among static cameras prior to distributed tracking based on a marginal information utility that accounts for both the communication quality and the local tracking confidence. Agents move on demand when hearing requests from nearby static cameras. Each agent independently selects its target with limited scene knowledge and computes its robotic control for collision-free active visual tracking. Collision avoidance among robots and targets can be achieved by the Optimal Reciprocal Collision Avoidance (ORCA) method. To further address view maintenance during collision avoidance manoeuvres, this thesis proposes an ORCA-based method with adaptive responsibility sharing and heading-aware robotic control mapping. Experimental results show that the proposed methods achieve higher tracking accuracy and better view maintenance compared with the state-of-the-art methods.Queen Mary University of London and Chinese Scholarship Council

Characterization of a Multi-User Indoor Positioning System Based on Low Cost Depth Vision (Kinect) for Monitoring Human Activity in a Smart Home

An increasing number of systems use indoor positioning for many scenarios such as asset tracking, health care, games, manufacturing, logistics, shopping, and security. Many technologies are available and the use of depth cameras is becoming more and more attractive as this kind of device becomes affordable and easy to handle. This paper contributes to the effort of creating an indoor positioning system based on low cost depth cameras (Kinect). A method is proposed to optimize the calibration of the depth cameras, to describe the multi-camera data fusion and to specify a global positioning projection to maintain the compatibility with outdoor positioning systems. The monitoring of the people trajectories at home is intended for the early detection of a shift in daily activities which highlights disabilities and loss of autonomy. This system is meant to improve homecare health management at home for a better end of life at a sustainable cost for the community

Cognitive visual tracking and camera control

Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision

leave a trace - A People Tracking System Meets Anomaly Detection

Video surveillance always had a negative connotation, among others because of the loss of privacy and because it may not automatically increase public safety. If it was able to detect atypical (i.e. dangerous) situations in real time, autonomously and anonymously, this could change. A prerequisite for this is a reliable automatic detection of possibly dangerous situations from video data. This is done classically by object extraction and tracking. From the derived trajectories, we then want to determine dangerous situations by detecting atypical trajectories. However, due to ethical considerations it is better to develop such a system on data without people being threatened or even harmed, plus with having them know that there is such a tracking system installed. Another important point is that these situations do not occur very often in real, public CCTV areas and may be captured properly even less. In the artistic project leave a trace the tracked objects, people in an atrium of a institutional building, become actor and thus part of the installation. Visualisation in real-time allows interaction by these actors, which in turn creates many atypical interaction situations on which we can develop our situation detection. The data set has evolved over three years and hence, is huge. In this article we describe the tracking system and several approaches for the detection of atypical trajectories

Robust 3D People Tracking and Positioning System in a Semi-Overlapped Multi-Camera Environment

People positioning and tracking in 3D indoor environments are challenging tasks due to background clutter and occlusions. Current works are focused on solving people occlusions in low-cluttered backgrounds, but fail in high-cluttered scenarios, specially when foreground objects occlude people. In this paper, a novel 3D people positioning and tracking system is presented, which shows itself robust to both possible occlusion sources: static scene objects and other people. The system holds on a set of multiple cameras with partially overlapped fields of view. Moving regions are segmented independently in each camera stream by means of a new background modeling strategy based on Gabor filters. People detection is carried out on these segmentations through a template-based correlation strategy. Detected people are tracked independently in each camera view by means of a graph-based matching strategy, which estimates the best correspondences between consecutive people segmentations. Finally, 3D tracking and positioning of people is achieved by geometrical consistency analysis over the tracked 2D candidates, using head position (instead of object centroids) to increase robustness to foreground occlusions

CED: Color Event Camera Dataset

Event cameras are novel, bio-inspired visual sensors, whose pixels output asynchronous and independent timestamped spikes at local intensity changes, called 'events'. Event cameras offer advantages over conventional frame-based cameras in terms of latency, high dynamic range (HDR) and temporal resolution. Until recently, event cameras have been limited to outputting events in the intensity channel, however, recent advances have resulted in the development of color event cameras, such as the Color-DAVIS346. In this work, we present and release the first Color Event Camera Dataset (CED), containing 50 minutes of footage with both color frames and events. CED features a wide variety of indoor and outdoor scenes, which we hope will help drive forward event-based vision research. We also present an extension of the event camera simulator ESIM that enables simulation of color events. Finally, we present an evaluation of three state-of-the-art image reconstruction methods that can be used to convert the Color-DAVIS346 into a continuous-time, HDR, color video camera to visualise the event stream, and for use in downstream vision applications.Comment: Conference on Computer Vision and Pattern Recognition Workshop

The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

Advantages of 3D time-of-flight range imaging cameras in machine vision applications

Machine vision using image processing of traditional intensity images is in wide spread use. In many situations environmental conditions or object colours or shades cannot be controlled, leading to difficulties in correctly processing the images and requiring complicated processing algorithms. Many of these complications can be avoided by using range image data, instead of intensity data. This is because range image data represents the physical properties of object location and shape, practically independently of object colour or shading. The advantages of range image processing are presented, along with three example applications that show how robust machine vision results can be obtained with relatively simple range image processing in real-time applications