Advanced Optimization Techniques for Convolutional Neural Networks in Real-Time Video Analytics Applications
Keywords:
Convolutional Neural Networks, real-time video analytics, network pruning, quantization, model compression, hardware-software co-designAbstract
Real-time video analytics has become a cornerstone in applications ranging from autonomous vehicles to surveillance systems. The computational demands of Convolutional Neural Networks (CNNs) in such scenarios require advanced optimization techniques to ensure efficiency without compromising accuracy. This paper reviews state-of-the-art optimization strategies for CNNs, including network pruning, quantization, model compression, and architectural innovations. Emphasis is placed on the trade-offs between computational efficiency and model performance. Additionally, the role of adaptive learning rates, knowledge distillation, and hardware-software co-design in achieving real-time analytics is discussed. The survey provides a comprehensive understanding of optimization strategies applicable in real-world scenarios, paving the way for further innovation in CNN-based video analytics.
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