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Multi-tiered Video Analytics for Abnormality Detection and Alerting

University of Houston


Use of video camera systems has become common across various public safety agencies. While manual review of captured video can be beneficial, there are a growing number of applications that would benefit from automated analyses of captured video. In the recent past, considerable attempts have been made towards video analytics for monitoring, e.g. analytics for automatic left object (baggage) detection, or line (perimeter) crossing are common today. While more advanced and sophisticated analytics can be designed and developed, the ingestion of resulting information to facilitate communication and timely response from first responders requires integration of video analytic methods with existing information management and communication systems. Typical video systems leverage a video management system (VMS) to record video from cameras and pushes event information into a public safety information management system (PSIM). The PSIM is often used as the information management and communication system to define standard operating processes for each event, which in turn facilitates planning and response. We have studied existing infrastructure of public safety video systems, both to understand its impact on design of video analytic solutions and to realize requirements on integration of developed solutions to enable its use by end users. - July 2019




Meet the Team

Principle Investigator: Shishir Shah
University of Houston


Project Description

The Public Safety community has made sizable investments into video technology, which will be leveraged with this project. The objective is to develop an intelligent, non-obtrusive, real-time continuous monitoring system for assessing activity and predicting emergent suspicious and criminal behavior across a network of distributed cameras. The University of Houston has already developed an analytics engine capable of detecting a number of situations requiring attention by an officer and evaluated it on their own campus. Building on this, there are three key objectives of this project.

 

University of Houston Logo
Credit: University of Houston

 

Applying analytics to real-life systems

Applying enhanced analytics to real-life Public Safety video systems can be challenging. In real deployments, cameras are often backhauled via wireless links, where packet loss and signal jitter impact video quality. This research will focus on tuning the analytics methodology to function in such environments and develop guidelines for video system deployment and configuration. Analytics will be enabled within the City of Houston’s Public Safety Video System, of which 80% of its 850+ cameras are connected wirelessly in an urban setting, and includes most of the regional public safety stakeholders as collaborators and users.

Operations workflow integration

A major challenge in large systems such as this is getting the right information to the right people at the right time. Officers have to be alerted once analytics identify an abnormal situation in view of a camera. Within the scope of this project, these alerts will be based on a multi-tiered video analytic framework and its utility will be evaluated for integration into the City’s public safety video system operational workflow based on PSIM solutions. Public Safety agencies all over the country use PSIM solutions of different vendors, making standardization very important.

Today, not many standards exist for analytics metadata. Guidelines will be identified where possible within this research to further foster the standards movement for analytics. Other U.S. Public Safety related organizations have expressed interest and   will be approached for trials of successful results of this technology.

Behavioral analytics

Fundamental analytics such as “perimeter crossing” are quite common and exist on most modern video analytic solutions. With this project, key public safety use cases will be explored and enhanced  behavioral analytics will be developed, putting the instincts of an experienced public safety officer into an algorithm, helping to identify threats before disaster strikes. Ideally, every camera on the network feeding into the analytics engine becomes an additional “experienced officer”. These additional eyes in the street represent a massive force multiplier. The research will use key members of the Houston public safety staff to develop and test these analytics.

<< Back to the PSIAP 2017 - Public Safety Analytics Page

Created September 28, 2017, Updated December 19, 2019