Visualization and Intelligent Systems Laboratory



Contact Information

Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425

Tel: (951)-827-3954

Bourns College of Engineering
NSF IGERT on Video Bioinformatics

UCR Collaborators:

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

Webmaster Contact Information:
Alex Shin

Last updated: July 1, 2017



Vehicle Modeling, Recognition, Detection, and Classification

Robust visual rear ground clearance estimation and classification of a passenger vehicle

Computation of Visual Rear Ground Clearance of vehicles was an important computer vision application. This problem was challenging as the road and vehicle rear bumper may have subtle appearance differences, vehicle motion was on uneven surfaces and there were real-time considerations. A method is presented to compute the Visual Rear Ground Clearance of a vehicle from its rear view video and classify it into two classes; namely Low Visual Rear Ground Clearance Vehicles and High Visual Rear Ground Clearance Vehicles. A multi-frame matching technique in conjunction with geometry based constraints was developed. It detected Regions-of-Interest ROIs of moving vehicles and moving shadows, and used shape constraints associated with vehicle geometry as viewed from its rear. It tracked stable features on a vehicle to compute the Visual Rear Ground Clearance.

Three-Dimensional Vehicle Model Building From Video

(a)–(c) SmartBG results of Jeep3. (d) Learning curve is for the entire 3-D model Traffic videos often capture slowly changing views of moving vehicles. We instead focus on 3-D model building vehicles with different shapes from a generic 3-D vehicle model by accumulating evidences in streaming traffic videos collected from a single camera. We propose a novel Bayesian graphical model (BGM), which is called structure-modifiable adaptive reason-building temporal Bayesian graph (SmartBG), that models uncertainty propagation in 3-D vehicle model building. Uncertainties are used as relative weights to fuse evidences and to compute the overall reliability of the generated models. Results from several traffic videos and two different view points demonstrate the performance of the method.

Structural Signatures for Passenger Vehicle Classification in Video

Multiframe matching. (a) Frame 1. (b) Frame 8. (c) Frame-to-frame cost for frames 1–8. (d) Multiframe cost for frames 1–8. In panels (c) and (d),darker (cooler) colors indicate lower cost, whereas brighter (warmer) colors indicate higher cost. This research focuses on a challenging pattern recognition problem of significant industrial impact, i.e., classifying vehicles from their rear videos as observed by a camera mounted on top of a highway with vehicles travelling at high speed. To solve this problem, we present a novel feature called structural signature. From a rear-view video, a structural signature recovers the vehicle side profile information, which is crucial in its classification. We present a complete system that computes structural signatures and uses them for classification of passenger vehicles into sedans, pickups, and minivans/sport utility vehicles in highway videos.

Vehicle Logo Super-Resolution by Canonical Correlation Analysis

Recognition of a vehicle make is of interest in the fields of law enforcement and surveillance. We have develop a canonical correlation analysis (CCA) based method for vehicle logo super-resolution to facilitate the recognition of the vehicle make. From a limited number of high-resolution logos, we populate the training dataset for each make using gamma transformations. Given a vehicle logo from a low resolution source (i.e., surveillance or traffic camera recordings), the learned models yield super-resolved results. By matching the low-resolution image and the generated high resolution images, we select the final output that is closest to the low-resolution image in the histogram of oriented gradients (HOG) feature space. Experimental results show that our approach outperforms the state-of-the-art super-resolution methods in qualitative and quantitative measures. Furthermore, the super-resolved logos help to improve the accuracy in the subsequent recognition tasks significantly.

Dynamic Bayesian Networks for Vehicle Classification in Video

Shadow removal and obtaining the bounding box. Vehicle classification has evolved into a significant subject of study due to its importance in autonomous navigation, traffic analysis, surveillance and security systems, and transportation management. We present a system which classifies a vehicle (given its direct rear-side view) into one of four classes Sedan, Pickup truck, SUV/Minivan, and unknown. A feature set of tail light and vehicle dimensions is extracted which feeds a feature selection algorithm. A feature vector is then processed by a Hybrid Dynamic Bayesian Network (HDBN) to classify each vehicle.

Incremental Unsupervised Three-Dimensional Vehicle Model Learning From Video

We introduce a new generic model-based approach for building 3-D models of vehicles from color video from a single uncalibrated traffic-surveillance camera. We propose a novel directional template method that uses trigonometric relations of the 2-D features and geometric relations of a single 3-D generic vehicle model to map 2-D features to 3-D in the face of projection and foreshortening effects. Results are shown for several simulated and real traffic videos in an uncontrolled setup. The performance of the proposed method for several types of vehicles in two considerably different traffic spots is very promising to encourage its applicability in 3-D reconstruction of other rigid objects in video.

Bayesian Based 3D Shape Reconstruction from Video

In a video sequence with a 3D rigid object moving, changing shapes of the 2D projections provide interrelated spatio-temporal cues for incremental 3D shape reconstruction. This research describes a probabilistic approach for intelligent view-integration to build 3D model of vehicles from traffic videos collected from an uncalibrated static camera. The proposed Bayesian net framework allows the handling of uncertainties in a systematic manner. The performance is verified with several types of vehicles in different videos.

Incremental Vehicle 3-D Modeling from Video

We present a new model-based approach for building 3-D models of vehicles from color video provided by a traffic surveillance camera. We incrementally build 3D models using a clustering technique. Geometrical relations based on 3D generic vehicle model map 2D features to 3D. The 3D features are then adaptively clustered over the frame sequence to incrementally generate the 3D model of the vehicle. Results are shown for both simulated and real traffic video. They are evaluated by a new structural performance measure underscoring usefulness of incremental learning.

Unsupervised Learning for Incremental 3-D Modeling

Learning based incremental 3D modeling of traffic vehicles from uncalibrated video data stream has enormous application potential in traffic monitoring and intelligent transportation systems. In this research, video data from a traffic surveillance camera is used to incrementally develop the 3D model of vehicles using a clustering based unsupervised learning. Geometrical relations based on 3D generic vehicle model map 2D features to 3D. The 3D features are then adaptively clustered over the frames to incrementally generate the 3D model of the vehicle. Results are shown for both simulated and real traffic video. They are evaluated by a structural performance measure.