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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.

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.    

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