Stereo Co-capture System for Recording and Tracking Fish with Frame- and
Event Cameras
- URL: http://arxiv.org/abs/2207.07332v1
- Date: Fri, 15 Jul 2022 08:04:10 GMT
- Title: Stereo Co-capture System for Recording and Tracking Fish with Frame- and
Event Cameras
- Authors: Friedhelm Hamann and Guillermo Gallego
- Abstract summary: We introduce a co-capture system for multi-animal visual data acquisition using conventional cameras and event cameras.
Event cameras offer multiple advantages over frame-based cameras, such as a high temporal resolution and temporal redundancy suppression.
We present an event-based multi-animal tracking algorithm, which proves the feasibility of the approach.
- Score: 11.87305195196131
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This work introduces a co-capture system for multi-animal visual data
acquisition using conventional cameras and event cameras. Event cameras offer
multiple advantages over frame-based cameras, such as a high temporal
resolution and temporal redundancy suppression, which enable us to efficiently
capture the fast and erratic movements of fish. We furthermore present an
event-based multi-animal tracking algorithm, which proves the feasibility of
the approach and sets the baseline for further exploration of combining the
advantages of event cameras and conventional cameras for multi-animal tracking.
Related papers
- BlinkTrack: Feature Tracking over 100 FPS via Events and Images [50.98675227695814]
We propose a novel framework, BlinkTrack, which integrates event data with RGB images for high-frequency feature tracking.
Our method extends the traditional Kalman filter into a learning-based framework, utilizing differentiable Kalman filters in both event and image branches.
Experimental results indicate that BlinkTrack significantly outperforms existing event-based methods.
arXiv Detail & Related papers (2024-09-26T15:54:18Z) - Investigating Event-Based Cameras for Video Frame Interpolation in Sports [59.755469098797406]
We present a first investigation of event-based Video Frame Interpolation (VFI) models for generating sports slow-motion videos.
Particularly, we design and implement a bi-camera recording setup, including an RGB and an event-based camera to capture sports videos, to temporally align and spatially register both cameras.
Our experimental validation demonstrates that TimeLens, an off-the-shelf event-based VFI model, can effectively generate slow-motion footage for sports videos.
arXiv Detail & Related papers (2024-07-02T15:39:08Z) - Enabling Cross-Camera Collaboration for Video Analytics on Distributed
Smart Cameras [7.609628915907225]
We present Argus, a distributed video analytics system with cross-camera collaboration on smart cameras.
We identify multi-camera, multi-target tracking as the primary task multi-camera video analytics and develop a novel technique that avoids redundant, processing-heavy tasks.
Argus reduces the number of object identifications and end-to-end latency by up to 7.13x and 2.19x compared to the state-of-the-art.
arXiv Detail & Related papers (2024-01-25T12:27:03Z) - EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms
with Real-captured Hybrid Dataset [55.12137324648253]
Event cameras are emerging imaging technology that offers advantages over conventional frame-based imaging sensors in dynamic range and sensing speed.
This paper focuses on five event-aided image and video enhancement tasks.
arXiv Detail & Related papers (2023-12-13T15:42:04Z) - Scalable and Real-time Multi-Camera Vehicle Detection,
Re-Identification, and Tracking [58.95210121654722]
We propose a real-time city-scale multi-camera vehicle tracking system that handles real-world, low-resolution CCTV instead of idealized and curated video streams.
Our method is ranked among the top five performers on the public leaderboard.
arXiv Detail & Related papers (2022-04-15T12:47:01Z) - TimeReplayer: Unlocking the Potential of Event Cameras for Video
Interpolation [78.99283105497489]
Event camera is a new device to enable video at the presence of arbitrarily complex motion.
This paper proposes a novel TimeReplayer algorithm to interpolate videos captured by commodity cameras with events.
arXiv Detail & Related papers (2022-03-25T18:57:42Z) - Asynchronous Multi-View SLAM [78.49842639404413]
Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice.
Our framework integrates a continuous-time motion model to relate information across asynchronous multi-frames during tracking, local mapping, and loop closing.
arXiv Detail & Related papers (2021-01-17T00:50:01Z) - Real-Time Face & Eye Tracking and Blink Detection using Event Cameras [3.842206880015537]
Event cameras contain emerging, neuromorphic vision sensors that capture local light intensity changes at each pixel, generating a stream of asynchronous events.
Driver monitoring systems (DMS) are in-cabin safety systems designed to sense and understand a drivers physical and cognitive state.
This paper proposes a novel method to simultaneously detect and track faces and eyes for driver monitoring.
arXiv Detail & Related papers (2020-10-16T10:02:41Z) - CONVINCE: Collaborative Cross-Camera Video Analytics at the Edge [1.5469452301122173]
This paper introduces CONVINCE, a new approach to look at cameras as a collective entity that enables collaborative video analytics pipeline among cameras.
Our results demonstrate that CONVINCE achieves an object identification accuracy of $sim$91%, by transmitting only about $sim$25% of all the recorded frames.
arXiv Detail & Related papers (2020-02-05T23:55:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.