7,078 research outputs found
A novel filter for block-based motion estimation
Noises, in the form of false motion vectors, cannot be avoided while capturing block motion vectors using block based motion estimation techniques. Similar noises are further introduced when the technique of global motion compensation is applied to obtain 'true' object motion from video sequences, where both the camera and object motions are present. We observe that the performance of the mean and the median filters in removing false motion vectors, for estimating 'true' object motion, is not satisfactory, especially when the size of the object is significantly smaller than the scene. In this paper we introduce a novel filter, named as the Mean-Accumulated-Thresholded (MAT) filter, in order to capture 'true' object motion vectors from video sequences with or without the camera motion (zoom and/or pan). Experimental results on representative standard video sequences are included to establish the superiority of our filter compared with the traditional median and mean filters
BSUV-Net: a fully-convolutional neural network for background subtraction of unseen videos
Background subtraction is a basic task in computer vision and video processing often applied as a pre-processing step for object tracking, people recognition, etc. Recently, a number of successful background-subtraction algorithms have been proposed, however nearly all of the top-performing ones are supervised. Crucially, their success relies upon the availability of some annotated frames of the test video during training. Consequently, their performance on completely “unseen” videos is undocumented in the literature. In this work, we propose a new, supervised, background subtraction algorithm for unseen videos (BSUV-Net) based on a fully-convolutional neural network. The input to our network consists of the current frame and two background frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance of overfitting, we also introduce a new data-augmentation technique which mitigates the impact of illumination difference between the background frames and the current frame. On the CDNet-2014 dataset, BSUV-Net outperforms stateof-the-art algorithms evaluated on unseen videos in terms of several metrics including F-measure, recall and precision.Accepted manuscrip
Nemo: a computational tool for analyzing nematode locomotion
The nematode Caenorhabditis elegans responds to an impressive range of
chemical, mechanical and thermal stimuli and is extensively used to investigate
the molecular mechanisms that mediate chemosensation, mechanotransduction and
thermosensation. The main behavioral output of these responses is manifested as
alterations in animal locomotion. Monitoring and examination of such
alterations requires tools to capture and quantify features of nematode
movement. In this paper, we introduce Nemo (nematode movement), a
computationally efficient and robust two-dimensional object tracking algorithm
for automated detection and analysis of C. elegans locomotion. This algorithm
enables precise measurement and feature extraction of nematode movement
components. In addition, we develop a Graphical User Interface designed to
facilitate processing and interpretation of movement data. While, in this
study, we focus on the simple sinusoidal locomotion of C. elegans, our approach
can be readily adapted to handle complicated locomotory behaviour patterns by
including additional movement characteristics and parameters subject to
quantification. Our software tool offers the capacity to extract, analyze and
measure nematode locomotion features by processing simple video files. By
allowing precise and quantitative assessment of behavioral traits, this tool
will assist the genetic dissection and elucidation of the molecular mechanisms
underlying specific behavioral responses.Comment: 12 pages, 2 figures. accepted by BMC Neuroscience 2007, 8:8
An Intelligent Monitoring System of Vehicles on Highway Traffic
Vehicle speed monitoring and management of highways is the critical problem
of the road in this modern age of growing technology and population. A poor
management results in frequent traffic jam, traffic rules violation and fatal
road accidents. Using traditional techniques of RADAR, LIDAR and LASAR to
address this problem is time-consuming, expensive and tedious. This paper
presents an efficient framework to produce a simple, cost efficient and
intelligent system for vehicle speed monitoring. The proposed method uses an HD
(High Definition) camera mounted on the road side either on a pole or on a
traffic signal for recording video frames. On the basis of these frames, a
vehicle can be tracked by using radius growing method, and its speed can be
calculated by calculating vehicle mask and its displacement in consecutive
frames. The method uses pattern recognition, digital image processing and
mathematical techniques for vehicle detection, tracking and speed calculation.
The validity of the proposed model is proved by testing it on different
highways.Comment: 5 page
- …