Accuracy-efficiency trade-off for using event-based data when performing bounding box-based object detection

Accuracy-efficiency trade-off for using event-based data when performing bounding box-based object detection

Benschop, Pascal (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Pattern Recognition and Bioinformatics)

Tömen, N. (mentor)
Strafforello, O. (mentor)
Liu, X. (mentor)
Cavalcante Siebert, L. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2022-06-22

Event-based cameras do not capture frames like an RGB camera, only data from pixels that detect a change in light intensity, making it a better alternative for processing videos. The sparse data acquired from event-based video only captures movement in an asynchronous way. In this paper an evaluation is made on the efficiency and accuracy of object detection, specifically localization, between sparse and dense representations of data. Convolutional Neural Networks are used to train and test on images and event-based data. The results show a positive trade-off in terms of accuracy and efficiency for using sparse event-based data instead of dense data like images. These results provide a basis for an argument to use event-based cameras instead of RGB cameras when dealing with object detection.

Object detection
Event-based vision
Trade-off Analysis

http://resolver.tudelft.nl/uuid:6b4536b1-27e9-4892-b26a-97148e701bc4

Student theses

bachelor thesis

© 2022 Pascal Benschop