As an alternative sensing paradigm, dynamic vision sensors (DVS) have been recently explored to tackle scenarios where conventional sensors result in high data rate and processing time. This paper presents a hybrid event-frame approach for detecting and tracking objects recorded by a stationary neuromorphic sensor, thereby exploiting the sparse DVS output in a low-power setting for traffic monitoring. Specifically, we propose a hardware efficient processing pipeline that optimizes memory and computational needs that enable long-term battery powered usage for IoT applications. To exploit the background removal property of a static DVS, we propose an event-based binary image creation that signals presence or absence of events in a frame duration. This reduces memory requirement and enables usage of simple algorithms like median filtering and connected component labeling for denoise and region proposal respectively. To overcome the fragmentation issue, a YOLO inspired neural network based detector and classifier to merge fragmented region proposals has been proposed. Finally, a new overlap based tracker was implemented, exploiting overlap between detections and tracks is proposed with heuristics to overcome occlusion. The proposed pipeline is evaluated with more than 5 hours of traffic recording spanning three different locations on two different neuromorphic sensors (DVS and CeleX) and demonstrate similar performance. Compared to existing event-based feature trackers, our method provides similar accuracy while needing approx 6 times less computes. To the best of our knowledge, this is the first time a stationary DVS based traffic monitoring solution is extensively compared to simultaneously recorded RGB frame-based methods while showing tremendous promise by outperforming state-of-the-art deep learning solutions.
翻译:作为一种替代感测模式,最近探索了动态视觉传感器(DVS),以解决常规传感器导致数据率和处理时间高的情景。本文介绍了一种混合事件范围方法,用以探测和跟踪由固定式神经形态传感器记录的物体,从而利用低功率环境中的稀疏DVS输出进行交通监测。具体地说,我们提议了一种硬件高效处理管道,以优化内存和计算需求,使IOT应用程序能够长期使用电池动力。为了利用静态DVS的背景去除属性,我们提议了一种基于事件的二进制图像,在框架期间出现或不发生事件。这样可以减少记忆需求,并使得能够使用简单的算法,例如中位过滤器和连接部件标签给Detenoise和地区提案使用。为了克服破碎问题,我们提议了由YOLO启发的神经网络探测器和分类,以整合分散的区域提案。最后,实施了一种新的基于重叠的跟踪器,利用基于检测和轨道的重叠,首次提出以克服隐蔽性解决方案。拟议的管道二进制图像,在框架期间存在信号存在或没有事件发生信号。这可以减少中位过滤要求,同时用S-Srevoral-S