Background: Transportation and vehicle classification systems are becoming smarter and more automated. For example, electronic toll collection systems have been introduced and drivers are not required to stop, eliminating road delays. New technologies have also been added to these systems that enable service providers to acquire data on what type of vehicles are utilizing their amenities as well as vehicle identification for safety & control purposes.  Brief Description: UCR Researchers have developed a method and system for vehicle classification using video imaging. This novel invention entails a vehicle ground clearance measurement system along with a video camera that captures a travelling vehicle and categorizes it into a vehicle class. The cameras on current methods and systems rely on side views of the vehicle, which can easily be obstructed by other vehicles.

Professor Bir Bhanu and colleagues at the  University of California, Riverside, have  developed a robust vehicle classification system based  on video images from the rear-side view of a vehicle. This  system classifies a vehicle into one of four  classes: sedan, pick-up truck, SUV/minivan,  and unknown. The system validates detected moving objects by a simple frame differencing approach. Table I shows the false alarm percentages over the different methods used to classify vehicles. Table II shows that the UCR method has the highest accuracy when compared to other known methods.       Figure 1 is the Dynamic Bayesian Network structure created by extracting data from surveillance. In conjunction with the car being spotted the Left Tail Light (LTL), License Plate (LP), Right Tail Light (RTL), and Rear Dimensions (RD) are identified simultaneously and set within the Dynamic Bayesian Network to accurately classify and identify the vehicle.    

Researchers led by Prof. Bir Bhanu at UCR have designed a patent pending system to automate the classification and analysis of player dribbling styles using an assembled dataset of soccer videos from various sources. Architecture for the classification of soccer dribbling styles.

Prof. Bir Bhanu and his colleagues from the University of California, Riverside have developed a method for  analyzing real-time video feed of vehicles from a rear  view  perspective to identify the make and model of a vehicle. This method works by using a software system for detecting the Regions-of-Interest (ROIs) of moving vehicles and moving shadows, computing structural and other features and using a vehicle make and model database for vehicle identification. The system performs calculations based on factors found in all vehicles, so it is reliable regardless of vehicle color and type. The system is compatible with low resolution video feed, so it is able to analyze video feed in real-time. Thus, this technology holds potential for innovating fields like vehicle surveillance, vehicle security, class-based vehicle tolling, and traffic monitoring where reliable real-time video analysis is needed. Two Step alignment: (a) Target image (b) Query Image, (c) Vertical alignment cost (Hotter colors indicate higher cost) and solution (blue line), (d) Horizontal alignment alignment cost and so- lution, (e) Aligned query, (f) Query aligned with SIFTflow Cumulative match characteristic for top 20 retrievals demonstrating significant improvement over the baseline

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