ABSTRACT

The paper presents a web based vision system using a networked IP camera for tracking objects of interest. Three critical issues are addressed in this paper. First is the detection of moving objects in the foreground; second is the control of pan-tilt-zoom (PTZ) IP cameras based on object location; and third is the collaboration of multiple cameras over the network to track objects of interests independently. The developed system utilized a network of PTZ cameras along with a number of software tools for this implementation. The system was able to track a single and multiple objects successfully. The difficulties in the detection of moving objects are also analyzed while multiple cameras are collaborating over a network utilizing PTZ cameras.

Keywords:

Internet Protocol Camera, Networked Camera, Vision Tracking, Tracking Handover, Image Processing, Collaborated Tracking

1. Introduction

An Internet protocol (IP) network camera allows remote viewing and monitoring from anywhere and anytime over the Internet. This is a very promising technology in the field of video surveillance. With the pan-tilt-zoom (PTZ) feature, an IP camera allows surveillance of large areas thereby reducing the number of fixed cameras required to cover a given area. An IP camera responds to commands through its integrated web server [1] . In most of the cases, the PTZ-IP cameras are either manually operated or programmed under timed operation, even when multiple cameras are deployed within an area. This makes it difficult to utilize them to their highest potential.

The issues for using PTZ-IP cameras for tracking are well summarized in reference [1] . The author highlighted the issue of improper response time within the camera’s web server, very low frame rates, and difficulty in image processing due to constantly changing backgrounds. A number of studies have been conducted in the area of tracking multiple objects using networked cameras. A system was designed that is able to discover spatial relationships among the camera’s fields of view and use this information to correspond to different perspective views of the same object [2] . In another effort, researchers tracked multiple moving objects using a single camera [3] . They used image segmentation and then identified humans in the image by computing the size of the blobs obtained. This is efficient only if the moving object is within the field of vision of the camera. However, using a PTZ camera for tracking multiple objects creates the challenge of locking on a single object of interest. Horesh and his co-authors reported a system where they use trajectories of objects moving around to predict and then detect the object’s next location [4] . They divided the ground surface into a large grid and used network flow to track the trajectories of multiple moving objects for differentiation. In one effort, researchers showed how they are tracking people using skin color segmentation [5] . They locked onto their targets using PTZ cameras and tracked their motion. However, as the system depended on skin color detection, their system was viable for tracking only two individuals. Chong and his co-workers tracked objects utilizing multiple PTZ cameras in collaboration [6] . In this work, each camera module had its own detection and tracking mechanism; however, the processing was done on a central network. This allowed the cameras to communicate among each other and allowed tracking of multiple objects. They use Kalman Consensus filters to address the issue of multiple object tracking. But it could not prioritize among the multiple targets. One effort reports a technique in which they were able to track objects across multiple cameras without overlapping views [7] . They utilized appearance model and space-time cues like velocity, location, and time to identify the moving target across multiple cameras. However, their system did not address the issue of using a PTZ camera and presence of multiple objects in the frame.

This paper describes a PTZ-IP surveillance system that overcomes some of these challenges. With the proposed technique, PTZ-IP cameras were programmed to detect and track moving objects and at the same time collaborate with other PTZ-IP cameras in the network so that the tracking can be done effectively and efficiently. In addition, the technique also addresses the challenge of identifying objects of interest that need to be tracked. The feature identifies the target of interest among a group of moving objects. Priority can be set based on identity, direction or motion, and time of a day. A centralized approach is proposed in which multiple PTZ-IP cameras are tracked an object of interest across the network. This paves a way for an advanced video surveillance in which an object of interest can be detected and tracked over a network of multiple PTZ-IP cameras. A feature described as “handing-off” tracking to the next PTZ-IP camera has also been introduced in which once a moving object is detected in the field of next camera in the network, the current camera hands off the tracking of that object.

2. System Design Strategy

Within the designed system, once the object is identified, the camera control algorithm changes the pan and tilt to track and follow the movement of the object. This process is running as a feedback loop that controls the speed of motion of the camera based on the speed of the moving object. When an object is detected within the Field of View (FOV) of Camera A, then Camera B pans itself towards the FOV of Camera A. The intention is to follow the moving object when it leaves the FOV of Camera A and enters the FOV of Camera B. In this way, cameras within the network try to track the object as long as the object is within the networked area. The tracking process is illustrated in Figure 1.

The system implementation involves a number of activities that includes control

of PTZ-IP cameras, moving object detection and tracking, tracking management, collaborated tracking, and prioritizing multiple object tracking. A system block diagram is shown in Figure 2. The identification of moving objects involves motion detection, background subtraction, region sampling, morphological filtering and blob detection. Two PTZ-IP Foscam cameras were used for the developed system. The same algorithm can be adopted and integrated for any number of PTZ-IP cameras.

3. Implementation

The implementation of the designed system involves a series of activities, such as image gathering, moving object detection, tracking, and handing over the object tracking to the next camera within the network. Figure 3 shows a flowchart of the tracking activities for a given camera. The section describes the details of each activity.

3.1. Control of PTZ-IP Cameras

Information about the installation and use of a Foscam PTZ-IP camera can be found from the manufacturer [8] . It automatically assigns itself a local IP that can be accessed using a network browser. Logging into the camera system using the provided IP allows access to the manufacturer-developed graphical user interface (GUI) through its web server. Controlling the PTZ-IP camera through the GUI is very simple and user-friendly; however, the challenge is to automate the camera control through the newly developed image processing system.

The pan and tilt of the camera were controlled by an image processing unit developed using C++ (on Visual Studio). The program ensured that the tracking object was at the center of the FOV of the camera. Figure 4 shows how the pan and tilt of the camera were adjusted to make the center of the FOV coincide with the object’s position. Figure 4(a) shows the initial position of the object and the camera’s FOV where the object is at one corner of the camera’s FOV. Figure 4(b) shows how the camera has automatically panned toward its left to make the object’s location coincide with the center of the camera’s FOV. Finally, in Figure 4(c) the camera has successfully matched its center with the object’s location.

3.2. Moving Object Detection

Background modeling is utilized for detection and tracking of a moving object. Research has presented different approaches for background modeling by background detection, subtraction, and motion cues [9] [10] [11] . For the proposed system we subtracted consecutive frames from the camera to identify moving objects (Figure 5). Subtraction of two images is performed straightforward in a single pass. The difference of image $P_1\left(i,j\right)$ and image $P_2\left(i,j\right)$ is given by Equation (1) where $Q\left(i,j\right)$ is the output image [12] .

$Q\left(i,j\right)=P_1\left(i,j\right)-P_2\left(i,j\right)$ (1)

The subtracted image f was sent through a morphological filter to remove noise and unwanted motion cues. Morphological filtering involved creating a threshold output (Figure 6) followed by erosion and dilation process with an 8 × 8 filter (Figure 7) [13] .

The erosion of an image $f\left(x\right)$ by a structural window

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