DESCRIPTION: This course addresses the design and implementation of high performance, embedded video processing systems. System design issues such as imager and VLSI technology, processor, memory, and I/O architectures, execution parallelism, image representations, and algorithms for scene recognition will be addressed in the context of a low-cost embedded surveillance system. Projects will include design and evaluation of a PC-based system implementation for specified surveillance applications (e.g., background/ foreground modeling, blob identification, object tracking, etc.).

COURSE OBJECTIVES: This course builds on prerequisite undergraduate material to apply recent research results in automatic scene analysis to embedded systems. Graduate students who complete the course will have a systems-level grasp of the complex interactions of algorithms, computing architectures, and technology. Example objectives include:

• impact of sensor quality and frame rate on processing requirements
• availability and exploitation of explicit parallelism in video surveillance algorithms
• color representation impact on storage and processing requirements
• impact of size-weight-power-cost constraints on system performance.

PROJECT DESCRIPTION: Students work in two-person teams to design and implement a basic automatic surveillance system. The target system analyses activity in dynamic indoor and outdoor scenes. Projects address different aspects of the overall system: (1) image capture and preprocessing, (2) background modeling, (3) object tracking, and (4) motion analysis, such as tracking people, computing pedestrian flow, and activity classification. Design teams calibrate and evaluate their systems in the field. Each project is evaluated on indoor and outdoor scenes in class-wide demonstration/discussion periods.

PROJECTS

READINGS

PREREQUISITES: graduate standing

TEXTBOOKS (recommended):
Multimedia Technology for Applications, Eds. Sheu and Ismail, 1998 [ISBN 0-7803-1174-4],
Machine Vision, Snyder and Qi, 2004 [ISBN 0-521-83046-X],
and recent research/survey papers.

CODE OF CONDUCT:

• Collaboration: Projects will be performed in two-person teams. Students are encouraged to discuss project assignments, systems issues, and algorithms with other students in the class, particularly during group class discussions. However, sharing or copying code or text between teams is prohibited. All student work should include author attribution.
• Scholarship: You must credit the source of all algorithms or code implementations you use in your projects that did not originate with you or your project partner. This should be included as citations in the project documentation (e.g., if you use the Stauffer-Grimson mixture-of-Gaussians background modeling algorithm, provide a reference to it in the project documentation).
• Field Work: In all activities associated with this class, particularly in gathering videos for test input data, do nothing to harm yourself or others (including risking personal injury, causing embarrassment, or invading privacy).

All conduct in this course will be governed by the Georgia Tech honor code. Additionally, it is expected that students will respect their peers and the instructor such that no one takes unfair advantage of anyone else associated with the course. Any suspected cases of academic dishonesty or nonacademic misconduct will be reported to the Dean of Students for further action.

INSTRUCTORS: Linda Wills and Scott Wills
OFFICES: Klaus Advanced Computing Building 3310 and 3312
PHONE: (404) 894-4565 and (404) 894-7469
E-MAIL: linda.wills@ece.gatech.edu and scott.wills@ece.gatech.edu

DETAILED TOPICAL OUTLINE:

1. Intro to Embedded Video Surveillance Systems
   • course objectives
   • summary of prerequisite material (what is needed for this class)
   • schedule and project team formation
   • anatomy of an embedded video processing system
2. Imagers
   • physics of CCD imagers
   • adding color (Beyer patterns, alternatives)
   • array design: resolution, sensitivity, dynamic range, noise
   • optical design: focal length, aperture, field of view
3. Front-End Conversion and Preprocessing
   • analog to digital conversion
   • image quality and correction
   • contrast, brightness, saturation, and color balance
   • using COTS cameras
   
4. Processing
   • pixel and scene element representations
   • parallel execution using COTS processors
   • parallel execution in FPGAs
   • energy efficiency: clock rate versus parallelism
5. Data I/O and Storage
   • video memory access patterns & requirements
   • stream caching versus DMA
   • caching versus recomputation tradeoffs
6. Communication
   • network transmission of data stream
   • network roundup: SATA, wired, wireless
7. Early Vision
   • variations in outdoor scenes (e.g., cloud effects, shadows, illumination variations)
   • edge detection
   • optical flow
   • feature extraction and tracking
   • clustering and segmentation
   
8. Background Modeling
   • multimodal models
   • adaptivity and selectivity in change detection
   • representation issues
   • non-parametric models
   • occlusion
9. Activity Modeling and Recognition
   • face detection and recognition
   • blob detection and correspondence
   • object classification and tracking
   • classifying activity sequences
   • anomaly detection
10. System Integration
    • functional specification
    • performance requirements
    • size-weight-power-cost
    • calibration and testing

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    For more information, please contact:

    Scott Wills / scott.wills@ece.gatech.edu / (404) 894-7469
    last revised on 20 August 2009 at 2:15 p.m.
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