加速事件驱动的监控视频系统特征检测与压缩
Accelerated Event-Based Feature Detection and Compression for Surveillance Video Systems
AI总结：
提出基于ADDER框架的监控视频系统，通过异步稀疏表示实现事件驱动特征检测，在VIRAT数据集上较OpenCV提升43.7%速度。
摘要：
监控视频的强时间一致性使得传统方法能够实现引人注目的压缩性能，但下游视觉应用以高数据速率处理解码后的图像帧。由于应用从压缩视频表示中提取时间冗余信息并不直接，我们提出了一种新颖系统，在稀疏的解压缩表示中传达时间冗余。我们利用名为ADDER的视频表示框架，将帧视频转码为稀疏、异步的强度样本。我们引入了内容自适应、有损压缩以及异步形式的经典视觉算法的机制。我们在VIRAT监控视频数据集上评估了我们的系统，并显示与OpenCV相比，FAST特征检测的中位速度提升了43.7%。我们运行与OpenCV相同的算法，但仅处理接收新异步事件的像素，而非处理图像帧中的每个像素。我们的工作为即将到来的神经形态传感器铺平了道路，并适用于未来使用脉冲神经网络的应用。
英文摘要：
The strong temporal consistency of surveillance video enables compelling compression performance with traditional methods, but downstream vision applications operate on decoded image frames with a high data rate. Since it is not straightforward for applications to extract information on temporal redundancy from the compressed video representations, we propose a novel system which conveys temporal redundancy within a sparse decompressed representation. We leverage a video representation framework called ADDER to transcode framed videos to sparse, asynchronous intensity samples. We introduce mechanisms for content adaptation, lossy compression, and asynchronous forms of classical vision algorithms. We evaluate our system on the VIRAT surveillance video dataset, and we show a median 43.7% speed improvement in FAST feature detection compared to OpenCV. We run the same algorithm as OpenCV, but only process pixels that receive new asynchronous events, rather than process every pixel in an image frame. Our work paves the way for upcoming neuromorphic sensors and is amenable to future applications with spiking neural networks.
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