Real-Time Video Analysis on an Embedded Smart Camera for Traffic Surveillance Presenter: Yu-Wei Fan

Outline Introduction System Architecture – Hardware – Software Algorithm – Stationary Vehicle Detection Algorithm Mapping – External memory access – Data transfer – Number format issues Computing Performance

Introduction Traffic surveillance consider about: Real time. Limited resources such like memory and power. The system include: CMOS image sensor Performs high-level video analysis Compresses the video stream using MPEG-4 Transfers the compressed data via an IP-based network to a base station

Outline Introduction System Architecture – Hardware – Software Algorithm – Stationary Vehicle Detection Algorithm Mapping – External memory access – Data transfer – Number format issues Computing Performance

Hardware

1.Video Sensor: LM-9618 CMOS sensor 2. Processing Unit : A rough estimation results in 10 GIPS computing performance. TMS320C6415 DSPs (600 MHz) 3.Communications Unit: Intel XScale IXP425 processor

Software DSPs: The DSP/BIOS real-time operating system operates the DSPs. Xscale: Linux (Kernel ) operates the network processor, allowing access to a broad variety of open source software modules.

Outline Introduction System Architecture – Hardware – Software Algorithm – Stationary Vehicle Detection Algorithm Mapping – External memory access – Data transfer – Number format issues Computing Performance

Stationary Vehicle Detection Requirements: 1.A stationary camera position 2.Pretty static ambient light conditions

Stationary Vehicle Detection Algorithm: 1. The statistics of the pixel’s intensity is computed and stored in the observation distribution (OD) matrix of size n × m. 2. The OD values are used to adapt the values of the background model (BG). 3. The algorithm identifies long-term intensity changes between the BG and the OD distribution. 4. If a connected component exceeds a predefined area a stationary vehicle has been identified. Typically : α=0.1 a=1

Outline Introduction System Architecture – Hardware – Software Algorithm – Stationary Vehicle Detection Algorithm Mapping – External memory access – Data transfer – Number format issues Computing Performance

External memory access Excessive access to external memory is a major source for poor performance on embedded DSP architectures. In many high-level languages memory management is hidden from the programmer.

External memory access For image data at full PAL resolution (720 × 576, 8-bit pixels), this results in a total of 7.12 MB of transferred data. The poor performance is poor. Consider the example:

External memory access The transferred data is reduced to 2.47 MB Image-based vs. pixel-based

Data transfer Direct memory access (DMA) to improve the memory transfer between the external memory and processor. A regular data access pattern is an important precondition for effective DMA. Use “ping-pong” buffers.

Number format issues Memory is a crucial resource in embedded systems. Especially, internal memory has to be handled very carefully. Parallelism can be improved by exploiting packed-data processing capabilities of current DSPs.

Outline Introduction System Architecture – Hardware – Software Algorithm – Stationary Vehicle Detection Algorithm Mapping – External memory access – Data transfer – Number format issues Computing Performance

1.SVD algorithm in Matlab 6.1R12 with 2.4 GHz Pentium 4 desktop computer. 2.C++ implementation directly derive from the SVD Matlab code. 3. Use algorithm mapping tactics.