Asynchronous event-based sensors, or “silicon retinae,” are a new class of vision sensors inspired by biological vision systems. The output of these sensors often contains a significant number of noise events along with the signal. Filtering these noise events is a common preprocessing step before using the data for tasks such as tracking and classification. This paper presents a novel spiking neural network-based approach to filtering noise events from data captured by an Asynchronous Time-based Image Sensor on a neuromorphic processor, the IBM TrueNorth Neurosynaptic System. The significant contribution of this work is that it demonstrates our proposed filtering algorithm outperforms the traditional nearest neighbor noise filter in achieving higher signal to noise ratio (~10 dB higher) and retaining the events related to signal (~3X more). In addition, for our envisioned application of object tracking and classification under some parameter settings, it can also generate some of the missing events in the spatial neighborhood of the signal for all classes of moving objects in the data which are unattainable using the nearest neighbor filter.
This paper presents a three layer spiking neural network based region proposal network operating on data generated by neuromorphic vision sensors. The proposed architecture consists of refractory, convolution and clustering layers designed with bio-realistic leaky integrate and fire (LIF) neurons and synapses. The proposed algorithm is tested on traffic scene recordings from a DAVIS sensor setup. The performance of the region proposal network has been compared with event based mean shift algorithm and is found to be far superior (≈ 50% better) in recall for similar precision (≈ 85%). Computational and memory complexity of the proposed method are also shown to be similar to that of event based mean shift.
In this paper, we present EBBIOT-a novel paradigm for object tracking using stationary neuromorphic vision sensors in low-power sensor nodes for the Internet of Video Things (IoVT). Different from fully event based tracking or fully frame based approaches, we propose a mixed approach where we create event-based binary images (EBBI) that can use memory efficient noise filtering algorithms. We exploit the motion triggering aspect of neuromorphic sensors to generate region proposals based on event density counts with > 1000X less memory and computes compared to frame based approaches. We also propose a simple overlap based tracker (OT) with prediction based handling of occlusion. Our overall approach requires 7X less memory and 3X less computations than conventional noise filtering and event based mean shift (EBMS) tracking. Finally, we show that our approach results in significantly higher precision and recall compared to EBMS approach as well as Kalman Filter tracker when evaluated over 1.1 hours of traffic recordings at two different locations.
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