Camera Handoff Research Articles

Mathematical model based traffic violations identification

Traffic rules violations and accidents on road are major issues now-a-days. Identification of vehicles violating traffic rules and manual monitoring of vehicles is difficult, due to traffic congestion on freeways. A novel mathematical model is proposed to generalize detection of a number of traffic violations on highways. The model, using image processing techniques translates lanes on the road as equation of lines. A tracking algorithm generates a vehicle trace which is modelled as equations. A piecewise linearity is used for the modelling and ease of computation of traffic violation. The model then solves a number of equations for finding intersection of traces with the traffic lanes to identify the violations. This novel modelling approach can help machine based identification of a number of traffic violations and proposed system need not to be installed in vehicles and all along road for violation detection. To cover larger length of the road, camera handoff algorithm is also designed. This technique keeps track of all vehicles along with their traces on Google maps.

Continuous camera placement using multiple objective optimisation process

Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility is inadequate for continuous and automated tracking. In such applications, a sufficient overlap between FOVs should be secured so that camera handoff can be executed successfully and automatically before the object of interest becomes untraceable or unidentifiable. In addition, most of the existing algorithms employ discrete solution space, which suffers from limited solution accuracy and high computational complexity due to the high dimension of sampled solution space. In this paper, we propose to perform the optimisation of the camera parameters in a continuous solution space. In addition, to incorporate the optimisation of coverage and sufficient overlapped FOVs, a weighted sum approach is utilised to translate a multiple objective optimisation problem into a single objective optimisation process in our previous work. Significantly improved handoff success rates are illustrated via experiments using typical office floor plans in comparison with Erdem and Sclaroff's method. Improved accuracy, enhanced robustness, completeness of the solution set, and reduced computational complexity are accomplished in comparison with our previous algorithms.

An approach to multi-agent interactive control in an intelligent space

A new type of multi-agent interactive control is proposed in an intelligent space system, which is based on heterogeneous multiple vision sensors. The vision sensors consist of two groups: static (fixed) cameras and dynamic (pan-tilt) cameras. The proposed system operates in two different modes according to the sensor groups. The system in each mode can detect and track the agents interactively. The first mode using static cameras concentrates on the localization accuracy of agent information. The second mode using dynamic cameras focuses on the robust tracking for the agent-of-interest. We also propose the interactive camera handoff methods for cooperative tracking for multiagents. The system performance is evaluated by practical experiments.

Reliability-based camera handoff for cooperative tracking with multiple pan-tilt cameras

We present a new handoff method for multiple pan-tilt cameras for mobile robot tracking in an indoor environment. Camera handoff is an important step to consistently maintain the visibility of a mobile robot with maximized object tracking accuracy. First, we propose a method to estimate the position of a mobile robot using single pan-tilt camera. Then, the concept of position reliability is defined to quantitatively evaluate the accuracy of position estimation and tracking ability of individual pan-tilt cameras. Position reliability is used to decide when to trigger handoff and who to response handoff in the proposed handoff algorithm. Experimental results demonstrate that four pan-tilt cameras can systematically track a mobile robot in an indoor environment using the proposed method.

Real-Time People Tracking in a Camera Network

We present an approach to track several subjects from video sequences acquired by multiple cameras in real time. We address the key concerns of real time performance and continuity of tracking in overlapping and nonoverlapping fields of view. Each human subject is represented by a parametric ellipsoid having a state vector that encodes its position, velocity and height. We also encode visibility and persistence to tackle problems of distraction and short-period occlusion. To improve likelihood computation from different viewpoints, including the relocation of subjects after network blind spots, the colored and textured surface of each ellipsoid is learned progressively as the subject moves through the scene. This is combined with the information about subject position and velocity to perform camera handoff. For real time performance, the boundary of the ellipsoid can be projected several hundred times per frame for comparison with the observation image. Further, our implementation employs a particle filter, developed for parallel implementation on a graphics processing unit. We have evaluated our algorithm on standard data sets using metrics for multiple object tracking accuracy (MOTA) and speed of processing, and can show significant improvements in comparison with published work.

Multi-Camera Tracking Helmet System

Object tracking in multiple cameras has been largely studied in the literature. However there are still unsolved problems, such as camera handoff, objects association, etc. In this paper we presents a novel multiple camera tracking helmet system, we stitch views from multiple cameras mounted on the helmet to one wide view in which we tracking objects, this simplifies the task of object tracking from multiple cameras. And combining with online training tracking algorithm, our tracking system demonstrates real-time and robust tracking performance. 

Re-Configuration Strategy for PTZ Camera Networks

A method for PTZ camera re-configuration oriented toward tracking applications and surveillance systems is presented. The visual constraints are transformed into geometric constraints by a coverage model, and the final PTZ configurations are averaged by a consensus algorithm. The approach is to design a distributed algorithm that enables cooperation between the cameras. Experimental results show successful camera handoff.

An efficient continuous tracking system in real-time surveillance application

A tracking object must present a proper field of view (FOV) in a multiple active camera surveillance system; its clarity can facilitate smooth processing by the surveillance system before further processing, such as face recognition. However, when pan–tilt–zoom (PTZ) cameras are used, the tracking object can be brought into the FOV by adjusting its intrinsic parameters; consequently, selection of the best-performing camera is critical. Performance is determined by the relative positions of the camera and the tracking objects, image quality, lighting and how much of the front side of the object faces the camera. In a multi-camera surveillance system, both camera hand-off and camera assignment play an important role in automated and persistent tracking, which are typical surveillance requirements. This study investigates the use of automatic methods for tracking an object across cameras in a surveillance network using PTZ cameras. An automatic, efficient continuous tracking scheme is developed. The goal is to determine the decision criteria for hand-off using Sight Quality Indication (SQI) (which includes information on the position of the object and the proportion of the front of object faces the camera), and to perform the camera handoff task in a manner that optimizes the vision effect associated with monitoring. Experimental results reveal that the proposed algorithm can be efficiently executed, and the handoff method for feasible and continuously tracking active objects under real-time surveillance.

Video Analysis in Pan-Tilt-Zoom Camera Networks

Pan-tilt-zoom (PTZ) cameras are able to dynamically modify their field of view (FOV). This functionality introduces new capabilities to camera networks such as increasing the resolution of moving targets and adapting the sensor coverage. On the other hand, PTZ functionality requires solutions to new challenges such as controlling the PTZ parameters, estimating the ego motion of the cameras, and calibrating the moving cameras.This tutorial provides an overview of the main video processing techniques and the currents trends in this active field of research. Autonomous PTZ cameras mainly aim to detect and track targets with the largest possible resolution. Autonomous PTZ operation is activated once the network detects and identifies an object as sensible target and requires accurate control of the PTZ parameters and coordination among the cameras in the network. Therefore, we present cooperative localization and tracking methods, i.e., multiagentand consensus-based approaches to jointly compute the target's properties such as ground-plane position and velocity. Stereo vision exploiting wide baselines can be used to derive three-dimensional (3-D) target localization. This tutorial further presents different techniques for controlling PTZ camera handoff, configuring the network to dynamically track targets, and optimizing the network configuration to increase coverage probability. It also discusses implementation aspects for these video processing techniques on embedded smart cameras, with a special focus on data access properties.

Camera handoff with adaptive resource management for multi-camera multi-object tracking

Camera handoff is a crucial step to obtain a continuously tracked and consistently labeled trajectory of the object of interest in multi-camera surveillance systems. Most existing camera handoff algorithms concentrate on data association, namely consistent labeling, where images of the same object are identified across different cameras. However, there exist many unsolved questions in developing an efficient camera handoff algorithm. In this paper, we first design a trackability measure to quantitatively evaluate the effectiveness of object tracking so that camera handoff can be triggered timely and the camera to which the object of interest is transferred can be selected optimally. Three components are considered: resolution, distance to the edge of the camera’s field of view (FOV), and occlusion. In addition, most existing real-time object tracking systems see a decrease in the frame rate as the number of tracked objects increases. To address this issue, our handoff algorithm employs an adaptive resource management mechanism to dynamically allocate cameras’ resources to multiple objects with different priorities so that the required minimum frame rate is maintained. Experimental results illustrate that the proposed camera handoff algorithm can achieve a substantially improved overall tracking rate by 20% in comparison with the algorithm presented by Khan and Shah.

Can You See Me Now? Sensor Positioning for Automated and Persistent Surveillance

Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility, which is a fundamental requirement of object tracking, is insufficient for automated persistent surveillance. In such applications, a continuous consistently labeled trajectory of the same object should be maintained across different camera views. Therefore, a sufficient uniform overlap between the cameras' FOVs should be secured so that camera handoff can successfully and automatically be executed before the object of interest becomes untraceable or unidentifiable. In this paper, we propose sensor-planning methods that improve existing algorithms by adding handoff rate analysis. Observation measures are designed for various types of cameras so that the proposed sensor-planning algorithm is general and applicable to scenarios with different types of cameras. The proposed sensor-planning algorithm preserves necessary uniform overlapped FOVs between adjacent cameras for an optimal balance between coverage and handoff success rate. In addition, special considerations such as resolution and frontal-view requirements are addressed using two approaches: 1) direct constraint and 2) adaptive weights. The resulting camera placement is compared with a reference algorithm published by Erdem and Sclaroff. Significantly improved handoff success rates and frontal-view percentages are illustrated via experiments using indoor and outdoor floor plans of various scales.

Camera handoff and placement for automated tracking systems with multiple omnidirectional cameras

In a multi-camera surveillance system, both camera handoff and placement play an important role in generating an automated and persistent object tracking, typical of most surveillance requirements. Camera handoff should comprise three fundamental components, time to trigger handoff process, the execution of consistent labeling, and the selection of the next optimal camera. In this paper, we design an observation measure to quantitatively formulate the effectiveness of object tracking so that we can trigger camera handoff timely and select the next camera appropriately before the tracked object falls out of the field of view (FOV) of the currently observing camera. In the meantime, we present a novel solution to the consistent labeling problem in omnidirectional cameras. A spatial mapping procedure is proposed to consider both the noise inherent to the tracking algorithms used by the system and the lens distortion introduced by omnidirectional cameras. This does not only avoid the tedious process, but also increases the accuracy, to obtain the correspondence between omnidirectional cameras without human interventions. We also propose to use the Wilcoxon Signed-Rank Test to improve the accuracy of trajectory association between pairs of objects. In addition, since we need a certain amount of time to successfully carry out the camera handoff procedure, we introduce an additional constraint to optimally reserve sufficient cameras’ overlapped FOVs for the camera placement. Experiments show that our proposed observation measure can quantitatively formulate the effectiveness of tracking, so that camera handoff can smoothly transfer objects of interest. Meanwhile, our proposed consistent labeling approach can perform as accurately as the geometry-based approach without tedious calibration processes and outperform Calderara’s homography-based approach. Our proposed camera placement method exhibits a significant increase in the camera handoff success rate at the cost of slightly decreased coverage, as compared to Erdem and Sclaroff’s method without considering the requirement on overlapped FOVs.