| 1. |
- Mohamed, S. A. S., et al.
(författare)
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Asynchronous Corner Tracking Algorithm Based on Lifetime of Events for DAVIS Cameras
- 2020
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Ingår i: 15th International Symposium on Visual Computing, ISVC 2020. - Cham : Springer Science and Business Media Deutschland GmbH. ; , s. 530-541
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Konferensbidrag (refereegranskat)abstract
- Event cameras, i.e., the Dynamic and Active-pixel Vision Sensor (DAVIS) ones, capture the intensity changes in the scene and generates a stream of events in an asynchronous fashion. The output rate of such cameras can reach up to 10 million events per second in high dynamic environments. DAVIS cameras use novel vision sensors that mimic human eyes. Their attractive attributes, such as high output rate, High Dynamic Range (HDR), and high pixel bandwidth, make them an ideal solution for applications that require high-frequency tracking. Moreover, applications that operate in challenging lighting scenarios can exploit from the high HDR of event cameras, i.e., 140 dB compared to 60 dB of traditional cameras. In this paper, a novel asynchronous corner tracking method is proposed that uses both events and intensity images captured by a DAVIS camera. The Harris algorithm is used to extract features, i.e., frame-corners from keyframes, i.e., intensity images. Afterward, a matching algorithm is used to extract event-corners from the stream of events. Events are solely used to perform asynchronous tracking until the next keyframe is captured. Neighboring events, within a window size of 5 × 5 pixels around the event-corner, are used to calculate the velocity and direction of extracted event-corners by fitting the 2D planar using a randomized Hough transform algorithm. Experimental evaluation showed that our approach is able to update the location of the extracted corners up to 100 times during the blind time of traditional cameras, i.e., between two consecutive intensity images.
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| 2. |
- Mohamed, S. A. S., et al.
(författare)
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Dynamic resource-aware corner detection for bio-inspired vision sensors
- 2020
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Ingår i: 2020 25th International Conference on Pattern Recognition, (ICPR). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 10465-10472
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Konferensbidrag (refereegranskat)abstract
- Event-based cameras are vision devices that transmit only brightness changes with low latency and ultra-low power consumption. Such characteristics make event-based cameras attractive in the field of localization and object tracking in resource-constrained systems. Since the number of generated events in such cameras is huge, the selection and filtering of the incoming events are beneficial from both increasing the accuracy of the features and reducing the computational load. In this paper, we present an algorithm to detect asynchronous corners form a stream of events in real-time on embedded systems. The algorithm is called the Three Layer Filtering-Harris or TLF-Harris algorithm. The algorithm is based on an events' filtering strategy whose purpose is 1) to increase the accuracy by deliberately eliminating some incoming events, i.e., noise and 2) to improve the real-time performance of the system, i.e., preserving a constant throughput in terms of input events per second, by discarding unnecessary events with a limited accuracy loss. An approximation of the Harris algorithm, in turn, is used to exploit its high-quality detection capability with a low-complexity implementation to enable seamless real-time performance on embedded computing platforms. The proposed algorithm is capable of selecting the best corner candidate among neighbors and achieves an average execution time savings of 59% compared with the conventional Harris score. Moreover, our approach outperforms the competing methods, such as eFAST, eHarris, and FA-Harris, in terms of real-time performance, and surpasses Arc* in terms of accuracy.
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| 3. |
- Yasin, J N, et al.
(författare)
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Navigation of Autonomous Swarm of Drones Using Translational Coordinates
- 2020
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer. ; , s. 353-362
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This work focuses on an autonomous swarm of drones, a multi-agent system, where the leader agent has the capability of intelligent decision making while the other agents in the swarm follow the leader blindly. The proposed algorithm helps with cost cutting especially in the multi-drone systems, i.e., swarms, by reducing the power consumption and processing requirements of each individual agent. It is shown that by applying a pre-specified formation design with feedback cross-referencing between the agents, the swarm as a whole can not only maintain the desired formation and navigate but also avoid collisions with obstacles and other drones. Furthermore, the power consumed by the nodes in the considered test scenario, is reduced by 50% by utilising the proposed methodology.
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