Low-Power Dynamic Object Detection and Classification With Freely Moving Event Cameras

Bharath Ramesh; Andrés Ussa; Luca Della Vedova; Hong Yang; Garrick Orchard

doi:10.3389/fnins.2020.00135

Low-Power Dynamic Object Detection and Classification With Freely Moving Event Cameras

Front Neurosci. 2020 Feb 20:14:135. doi: 10.3389/fnins.2020.00135. eCollection 2020.

Authors

Bharath Ramesh^{1

2}, Andrés Ussa^{1

2}, Luca Della Vedova², Hong Yang², Garrick Orchard^{1

2}

Affiliations

¹ Life Science Institute, The N.1 Institute for Health, National University of Singapore, Singapore, Singapore.
² Temasek Laboratories, National University of Singapore, Singapore, Singapore.

Abstract

We present the first purely event-based, energy-efficient approach for dynamic object detection and categorization with a freely moving event camera. Compared to traditional cameras, event-based object recognition systems are considerably behind in terms of accuracy and algorithmic maturity. To this end, this paper presents an event-based feature extraction method devised by accumulating local activity across the image frame and then applying principal component analysis (PCA) to the normalized neighborhood region. Subsequently, we propose a backtracking-free k-d tree mechanism for efficient feature matching by taking advantage of the low-dimensionality of the feature representation. Additionally, the proposed k-d tree mechanism allows for feature selection to obtain a lower-dimensional object representation when hardware resources are limited to implement PCA. Consequently, the proposed system can be realized on a field-programmable gate array (FPGA) device leading to high performance over resource ratio. The proposed system is tested on real-world event-based datasets for object categorization, showing superior classification performance compared to state-of-the-art algorithms. Additionally, we verified the real-time FPGA performance of the proposed object detection method, trained with limited data as opposed to deep learning methods, under a closed-loop aerial vehicle flight mode. We also compare the proposed object categorization framework to pre-trained convolutional neural networks using transfer learning and highlight the drawbacks of using frame-based sensors under dynamic camera motion. Finally, we provide critical insights about the feature extraction method and the classification parameters on the system performance, which aids in understanding the framework to suit various low-power (less than a few watts) application scenarios.

Keywords: FIFO processing; closed-loop control; event-based descriptor; low-power FPGA; neuromorphic vision; object detection; object recognition; rectangular grid.