Moving Target Detection Based on FPGA

Moving Target Detection is a critical task in applications such as video surveillance, autonomous vehicles, and robotics. Implementing moving target detection on an FPGA (Field-Programmable Gate Array) offers significant advantages, including real-time processing, low power consumption, and high parallelism. Below is a detailed explanation of how to design and implement a moving target detection system on an FPGA.

1. Overview of Moving Target Detection

Moving target detection involves identifying and tracking objects that are in motion within a sequence of video frames. Common techniques include:

• Frame Differencing: Detects changes between consecutive frames.
• Background Subtraction: Compares the current frame to a background model.
• Optical Flow: Tracks the motion of pixels between frames.

2. Why Use FPGAs for Moving Target Detection?

• Real-Time Processing: FPGAs can process video streams at high frame rates with low latency.
• Parallelism: FPGAs can perform multiple operations simultaneously, making them ideal for pixel-level processing.
• Low Power Consumption: FPGAs are more power-efficient than GPUs or CPUs for many tasks.
• Customizability: FPGAs allow for tailored hardware designs to meet specific application requirements.

3. System Design for Moving Target Detection on FPGA
The system can be divided into the following modules:

1. Video Input Interface:

Captures video frames from a camera (e.g., HDMI, MIPI, or parallel interface).

1. Preprocessing:

Converts the video frames to grayscale and resizes them if necessary.

1. Background Subtraction:

Compares the current frame to a background model to detect moving objects.

1. Object Detection:

Identifies and labels moving objects using techniques like thresholding or connected component analysis.

1. Output Interface:

Displays the processed video with detected objects highlighted.

4. Implementation Steps
Step 1: Video Input Interface

• Use an FPGA IP core or custom logic to interface with the camera.
• Example: Capture frames from a parallel camera interface and store them in on-chip memory (BRAM).

Step 2: Preprocessing
Convert the video frames to grayscale to reduce computational complexity.

Example in Verilog:

verilog

module grayscale_converter (
    input wire [7:0] red, green, blue,
    output reg [7:0] gray
);
    always @(*) begin
        gray = (red + green + blue) / 3; // Simple grayscale conversion
    end
endmodule

Step 3: Background Subtraction
Maintain a background model (e.g., average of previous frames).

Subtract the current frame from the background model to detect changes.

Example in Verilog:

verilog

module background_subtraction (
    input wire clk,
    input wire [7:0] current_frame,
    output reg [7:0] difference
);
    reg [7:0] background_model;

    always @(posedge clk) begin
        difference = current_frame - background_model; // Compute difference
        background_model <= (background_model + current_frame) / 2; // Update background model
    end
endmodule

Step 4: Object Detection
Apply a threshold to the difference image to identify moving objects.

Use connected component analysis to label and track objects.

Example in Verilog:

verilog

module object_detection (
    input wire clk,
    input wire [7:0] difference,
    output reg object_detected
);
    reg [7:0] threshold = 8'h20; // Set a threshold

    always @(posedge clk) begin
        object_detected = (difference > threshold) ? 1 : 0; // Detect objects
    end
endmodule

Step 5: Output Interface

• Highlight detected objects on the output video stream.
• Example: Overlay bounding boxes or color highlights on the original frame.

5. Example: Frame Differencing in Verilog
Here’s a simplified example of frame differencing for moving target detection:

verilog

module frame_differencing (
    input wire clk,
    input wire [7:0] current_frame,
    input wire [7:0] previous_frame,
    output reg [7:0] difference
);
    always @(posedge clk) begin
        difference = (current_frame > previous_frame) ? (current_frame - previous_frame) : (previous_frame - current_frame);
    end
endmodule

6. Optimization Techniques

1. Pipelining:

Break down the processing stages into smaller, parallelizable steps to increase throughput.

1. Parallel Processing:

Process multiple pixels simultaneously using FPGA parallelism.

1. Memory Optimization:

Use on-chip memory (BRAM) to store frames and intermediate results.

1. Fixed-Point Arithmetic:

Use fixed-point arithmetic instead of floating-point to reduce resource usage.

7. Tools and Libraries

• Xilinx Vivado: For FPGA synthesis and implementation.
• Intel Quartus: For Intel FPGA designs.
• OpenCV: For algorithm development and testing on a PC before FPGA implementation.
• HLS (High-Level Synthesis): To design algorithms in C/C++ and convert them to HDL.

8. Applications

1. Video Surveillance:

Detect intruders or suspicious activities in real-time.

1. Autonomous Vehicles:

Identify and track moving objects like pedestrians and vehicles.

1. Traffic Monitoring:

Analyze vehicle movement and detect traffic violations.

1. Industrial Automation:

Monitor conveyor belts for moving objects or defects.

9. Challenges

1. Resource Constraints:

FPGAs have limited resources, so efficient design is crucial.

1. Algorithm Complexity:

Complex algorithms may require significant optimization for FPGA implementation.

1. Real-Time Requirements:

Ensuring low latency and high throughput can be challenging.

Conclusion

Implementing moving target detection on an FPGA enables real-time, low-power, and high-performance solutions for a wide range of applications. By leveraging FPGA parallelism and optimizing the design, you can achieve efficient and accurate detection of moving objects in video streams. This approach is particularly valuable in scenarios where real-time processing and low power consumption are critical.