Search

Your search keyword '"computer vision community"' showing total 2 results
Start Over Descriptor "computer vision community" Topic motion estimation
2 results on '"computer vision community"'

1. Improved strategy for human action recognition; experiencing a cascaded design.

Author

Khan, Muhammad Attique, Akram, Tallha, Sharif, Muhammad, Muhammad, Nazeer, Javed, Muhammad Younus, and Naqvi, Syed Rameez

Abstract

Human motion analysis has received a lot of attention in the computer vision community during the last few years. This research domain is supported by a wide spectrum of applications including video surveillance, patient monitoring systems, and pedestrian detection, to name a few. In this study, an improved cascaded design for human motion analysis is presented; it consolidates four phases: (i) acquisition and preprocessing, (ii) frame segmentation, (iii) features extraction and dimensionality reduction, and (iv) classification. The implemented architecture takes advantage of CIE‐Lab and National Television System Committee colour spaces, and also performs contrast stretching using the proposed red–green–blue* colour space enhancement technique. A parallel design utilising attention‐based motion estimation and segmentation module is also proposed in order to avoid the detection of false moving regions. In addition to these contributions, the proposed feature selection technique called entropy controlled principal components with weights minimisation, further improves the classification accuracy. The authors claims are supported with a comparison between six state‐of‐the‐art classifiers tested on five standard benchmark data sets including Weizmann, KTH, UIUC, Muhavi, and WVU, where the results reveal an improved correct classification rate of 96.55, 99.50, 99.40, 100, and 100%, respectively. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

2. Multi-body Motion Estimation from Monocular Vehicle-Mounted Cameras

Author

Davide Scaramuzza, Reza Sabzevari, and University of Zurich

Subjects
multiple moving object localization, 0209 industrial biotechnology, simultaneous localization and mapping (SLAM), Computer science, 02 engineering and technology, Kinematics, Simultaneous localization and mapping, computer vision, matrix decomposition, unconstrained motion, SLAM (robots), motion estimation, 020901 industrial engineering & automation, computer vision community, multibody motion estimation, 0202 electrical engineering, electronic engineering, information engineering, Structure from motion, Computer vision, image segmentation, Tracking, object detection, simultaneous multiple moving object, Computer Science Applications, vehicle kinematic constraints, 020201 artificial intelligence & image processing, Motion segmentation, trajectory matrix factorization, 10009 Department of Informatics, reprojection error, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 2207 Control and Systems Engineering, multibody structure from motion, 000 Computer science, knowledge & systems, Match moving, camera ego motion, cameras, Motion estimation, 1706 Computer Science Applications, Electrical and Electronic Engineering, Visual odometry, noisy image measurements, level image dataset, ComputingMethodologies_COMPUTERGRAPHICS, street, projective multiple, business.industry, 2208 Electrical and Electronic Engineering, mounted cameras, vehicle ego motion estimation, constrained motion model, monocular vehicle, Vehicles, eoru motions, Object detection, urban environments, robot localization pipeline, Motion field, Control and Systems Engineering, multi, eoru, Artificial intelligence, business, Estimation, body structure from motion
Abstract

This paper addresses the problem of simultaneous estimation of a vehicle's ego motion and motions of multiple moving objects in the scene–called eoru motions –through a monocular vehicle-mounted camera. Localization of multiple moving objects and estimation of their motions is crucial for autonomous vehicles. Conventional localization and mapping techniques (e.g., visual odometry and simultaneous localization and mapping) can only estimate the ego motion of the vehicle. The capability of a robot localization pipeline to deal with multiple motions has not been widely investigated in the literature. We present a theoretical framework for robust estimation of multiple relative motions in addition to the camera ego motion. First, the framework for general unconstrained motion is introduced and then it is adapted to exploit the vehicle kinematic constraints to increase efficiency. The method is based on projective factorization of the multiple-trajectory matrix . First, the ego motion is segmented and then several hypotheses are generated for the eoru motions . All the hypotheses are evaluated and the one with the smallest reprojection error is selected. The proposed framework does not need any a priori knowledge of the number of motions and is robust to noisy image measurements. The method with a constrained motion model is evaluated on a popular street-level image dataset collected in urban environments (the KITTI dataset), including several relative ego-motion and eoru-motion scenarios. A benchmark dataset ( Hopkins 155 ) is used to evaluate this method with a general motion model. The results are compared with those of the state-of-the-art methods considering a similar problem, referred to as multibody structure from motion in the computer vision community.

Published
2016
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results