Moving Object Detection for Event-based Vision using k-means Clustering

@article{Mondal2021MovingOD,
  title={Moving Object Detection for Event-based Vision using k-means Clustering},
  author={Anindya Mondal and Mayukhmali Das},
  journal={2021 IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)},
  year={2021},
  pages={1-6}
}
  • Anindya Mondal, M. Das
  • Published 4 September 2021
  • Computer Science
  • 2021 IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)
Moving object detection is a crucial task in computer vision. Event-based cameras are bio-inspired cameras that mimic the working of the human eye. Unlike conventional frame-based cameras, these cameras pose multiple advantages, like reduced latency, HDR, reduced motion blur during high motion, low power consumption, etc. However, these advantages come at a high cost, as event-based cameras are sensitive to noise and have low resolution. Moreover, for the lack of useful visual features like… 
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References

SHOWING 1-10 OF 33 REFERENCES

Event-Based Moving Object Detection and Tracking

This paper presents a novel event stream representation which enables us to utilize information about the dynamic (temporal)component of the event stream, and demonstrates the framework on the task of independent motion detection and tracking, where it is used to locate differently moving objects in challenging situations of very fast motion.

Event-based Motion Segmentation with Spatio-Temporal Graph Cuts

This work develops a method to identify independently moving objects acquired with an event-based camera and jointly solves two sub-problems, namely event-cluster assignment (labeling) and motion model fitting, in an iterative manner by exploiting the structure of the input event data in the form of a spatio-temporal graph.

Moving object detection: Review of recent research trends

A brief classification of the classical approaches for moving object detection is provided and recent research trends to detect moving object for single stationary camera is reviewed with discussion of key points and limitations of each approach.

Event-Based Vision: A Survey

This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras.

Online Multi-object Tracking-by-Clustering for Intelligent Transportation System with Neuromorphic Vision Sensor

This contribution proposes an online multi-target tracking system utilizing for neuromorphic vision sensors, which is the first neuromorph vision system in intelligent transportation systems, and integrates an online tracking-by-clustering system running at a high frame rate, which far exceeds the real-time capabilities of traditional frame based industry cameras.

Moving Object Detection With Deep CNNs

To solve the problem of noise-induced object fracture during the coarse-grained detection process, a low-complexity connected region detection algorithm is presented to extract moving regions and Deep Convolution Neural Networks are used to detect more precise coordinates and identify the category of objects.

Graph Moving Object Segmentation

This work proposes a new algorithm that is composed of segmentation, background initialization, graph construction, unseen sampling, and a semi-supervised learning method inspired by the theory of recovery of graph signals, which has the advantage of requiring less labeled data than deep learning methods while having competitive results on both static and moving camera videos.

Research on the Detection and Tracking Algorithm of Moving Object in Image Based on Computer Vision Technology

The research results show that the algorithm proposed in this paper has the longest running time per frame when tracking a moving target, which is about 2.3 times that of the single frame running time of the CamShift algorithm.

Deep Learning based Moving Object Detection for Video Surveillance

A new two-stream neural network which combines the traditional background modeling method with a deep learning network to detect moving objects to solve the problem of how to distinguish moving objects from static objects.

Spatiotemporal multiple persons tracking using Dynamic Vision Sensor

An algorithm for spatiotemporal tracking that is suitable for Dynamic Vision Sensor is introduced and the problem of multiple persons tracking in the occurrence of high occlusions is addressed.