Video Sequence Object Tracking Using CNN and DSP Implementation

Object tracking technology in video sequences based on CNN and DSP

Object tracking is a crucial research direction in computer vision, widely applied in surveillance, autonomous driving, and human-computer interaction scenarios. This article introduces how to combine Convolutional Neural Networks (CNN) and Digital Signal Processors (DSP) to achieve efficient video sequence object tracking, with algorithm effectiveness validated through MATLAB simulations.

The Role of CNN in Object Tracking Convolutional Neural Networks extract deep features of targets through multi-layer convolution and pooling operations, effectively handling complex scenarios like lighting variations and occlusions. In video sequences, CNN can perform feature extraction on each frame and match them with target features from initial or previous frames, enabling continuous target localization. Implementation typically involves using MATLAB's Deep Learning Toolbox with functions like `activations()` for feature extraction from pre-trained networks.

DSP Acceleration Optimization Digital Signal Processors (DSP) excel at parallel computing and can efficiently handle convolution operations in CNNs. Deploying CNN models to DSP platforms significantly improves real-time tracking performance. DSP's fixed-point arithmetic and memory optimization techniques further reduce power consumption, making them suitable for embedded applications. Code generation can be achieved using MATLAB Coder with DSP-specific optimization flags.

MATLAB Simulation Workflow Implementing this technology in MATLAB typically involves: First, utilizing pre-trained CNNs (such as YOLO or Siamese networks) for target feature extraction using functions like `yolov4ObjectDetector`; then designing similarity measurement functions (like correlation coefficients) for inter-frame matching; finally porting algorithms to hardware platforms using DSP code generation tools. Simulations can incorporate motion blur or noise interference to validate algorithm robustness, implemented through `imgaussfilt()` for noise addition and `imfilter()` for blur effects.

Extended Applications of Cellular Neural Networks Cellular Neural Networks (a variant of CNN) are particularly suitable for image processing tasks due to their local connectivity characteristics. In object tracking, customized cellular structures can enhance edge or texture feature extraction capabilities, improving tracking performance for small targets or low-contrast scenarios. This can be implemented using custom layer definitions in MATLAB's Deep Learning Toolbox.

Optimization Directions and Challenges Real-time Optimization: Balancing CNN depth with DSP computational constraints requires model pruning using `reduceNetwork()`; Accuracy Improvement: Multi-feature fusion or optical flow assistance can reduce tracking loss, implemented through `opticalFlowHS` function; Hardware Deployment: Quantizing and compressing models using `quantize()` to adapt to DSP memory limitations.

This solution provides a feasible implementation path for embedded vision systems through hardware-software co-design. Developers can adjust network architectures or DSP instruction sets according to specific scenarios to further optimize performance, leveraging MATLAB's embedded deployment capabilities.