1、A System for Video Surveillance and Monitoring The thrust of CMU research under the DARPA Video Surveillance and Monitoring (VSAM) project is cooperative multi-sensor surveillance to support battlefield awareness. Under our VSAM Integrated Feasibility Demonstration (IFD) contract, we have developed
2、automated video understanding technology that enables a single human operator to monitor activities over a complex area using a distributed network of active video sensors. The goal is to automatically collect and disseminate real-time information from the battlefield to improve the situational awar
3、eness of commanders and staff. Other military and federal law enforcement applications include providing perimeter security for troops, monitoring peace treaties or refugee movements from unmanned air vehicles, providing security for embassies or airports, and staking out suspected drug or terrorist
4、 hide-outs by collecting time-stamped pictures of everyone entering and exiting the building. Automated video surveillance is an important research area in the commercial sector as well. Technology has reached a stage where mounting cameras to capture video imagery is cheap, but finding available hu
5、man resources to sit and watch that imagery is expensive. Surveillance cameras are already prevalent in commercial establishments, with camera output being recorded to tapes that are either rewritten periodically or stored in video archives. After a crime occurs a store is robbed or a car is stolen
6、investigators can go back after the fact to see what happened, but of course by then it is too late. What is needed is continuous 24-hour monitoring and analysis of video surveillance data to alert security officers to a burglary in progress, or to a suspicious individual loitering in the parking lo
7、t, while options are still open for avoiding the crime. Keeping track of people, vehicles, and their interactions in an urban or battlefield environment is a difficult task. The role of VSAM video understanding technology in achieving this goal is to automatically “parse” people and vehicles from ra
8、w video, determine their geolocations, and insert them into dynamic scene visualization. We have developed robust routines for detecting and tracking moving objects. Detected objects are classified into semantic categories such as human, human group, car, and truck using shape and color analysis, an
9、d these labels are used to improve tracking using temporal consistency constraints. Further classification of human activity, such as walking and running, has also been achieved. Geolocations of labeled entities are determined from their image coordinates using either wide-baseline stereo from two o
10、r more overlapping camera views, or intersection of viewing rays with a terrain model from monocular views. These computed locations feed into a higher level tracking module that tasks multiple sensors with variable pan, tilt and zoom to cooperatively and continuously track an object through the sce
11、ne. All resulting object hypotheses from all sensors are transmitted as symbolic data packets back to a central operator control unit, where they are displayed on a graphical user interface to give a broad overview of scene activities. These technologies have been demonstrated through a series of ye
12、arly demos, using a testbed system developed on the urban campus of CMU. Detection of moving objects in video streams is known to be a significant, and difficult, research problem. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detec
13、ting moving blobs provides a focus of attention for recognition, classification, and activity analysis, making these later processes more efficient since only “moving” pixels need be considered. There are three conventional approaches to moving object detection: temporal differencing ； background su
14、btraction； and optical flow. Temporal differencing is very adaptive to dynamic environments, but generally does a poor job of extracting all relevant feature pixels. Background subtraction provides the most complete feature data, but is extremely sensitive to dynamic scene changes due to lighting an
15、d extraneous events. Optical flow can be used to detect independently moving objects in the presence of camera motion； however, most optical flow computation methods are computationally complex, and cannot be applied to full-frame video streams in real-time without specialized hardware. Under the VS
16、AM program, CMU has developed and implemented three methods for moving object detection on the VSAM testbed. The first is a combination of adaptive background subtraction and three-frame differencing . This hybrid algorithm is very fast, and surprisingly effective indeed, it is the primary algorithm
17、 used by the majority of the SPUs in the VSAM system. In addition, two new prototype algorithms have been developed to address shortcomings of this standard approach. First, a mechanism for maintaining temporal object layers is developed to allow greater disambiguation of moving objects that stop fo
18、r a while, are occluded by other objects, and that then resume motion. One limitation that affects both this method and the standard algorithm is that they only work for static cameras, or in a ”stepand stare” mode for pan-tilt cameras. To overcome this limitation, a second extension has beendevelop
19、ed to allow background subtraction from a continuously panning and tilting camera . Through clever accumulation of image evidence, this algorithm can be implemented in real-time on a conventional PC platform. A fourth approach to moving object detection from a moving airborne platform has also been
20、developed, under a subcontract to the Sarnoff Corporation. This approach is based on image stabilization using special video processing hardware. The current VSAM IFD testbed system and suite of video understanding technologies are the end result of a three-year, evolutionary process. Impetus for th
21、is evolution was provided by a series of yearly demonstrations. The following tables provide a succinct synopsis of the progress made during the last three years in the areas of video understanding technology, VSAM testbed architecture, sensor control algorithms, and degree of user interaction. Alth
22、ough the program is over now, the VSAM IFD testbed continues to provide a valuable resource for the development and testing of new video understanding capabilities. Future work will be directed towards achieving the following goals: l better understanding of human motion, including segmentation and
23、tracking of articulated body parts； l improved data logging and retrieval mechanisms to support 24/7 system operations； l bootstrapping functional site models through passive observation of scene activities； l better detection and classification of multi-agent events and activities； l better camera control to enable smooth object tracking at high zoom； and l acquisition and selection of “best views” with the eventual goal of recognizing individuals in the scene.
