20,710 research outputs found
Utilization of Robust Video Processing Techniques to Aid Efficient Object Detection and Tracking
AbstractIn this research, data acquired by Unmanned Aerial Vehicles (UAVs) are primarily used to detect and track moving objects which pose a major security threat along the United States southern border. Factors such as camera motion, poor illumination and noise make the detection and tracking of moving objects in surveillance videos a formidable task. The main objective of this research is to provide a less ambiguous image data for object detection and tracking by means of noise reduction, image enhancement, video stabilization, and illumination restoration. The improved data is later utilized to detect and track moving objects in surveillance videos. An optimization based image enhancement scheme was successfully implemented to increase edge information to facilitate object detection. Noise present in the raw video captured by the UAV was efficiently removed using search and match methodology. Undesired motion induced in the video frames was eliminated using block matching technique. Moving objects were detected and tracked by using contour information resulting from the implementation of adaptive background subtraction based detection process. Our simulation results shows the efficiency of these algorithms in processing noisy, un-stabilized raw video sequences which were utilized to detect and track moving objects in the video sequences
Moving Object Detection and Segmentation for Remote Aerial Video Surveillance
Unmanned Aerial Vehicles (UAVs) equipped with video cameras are a flexible support to ensure civil and military safety and security. In this thesis, a video processing chain is presented for moving object detection in aerial video surveillance. A Track-Before-Detect (TBD) algorithm is applied to detect motion that is independent of the camera motion. Novel robust and fast object detection and segmentation approaches improve the baseline TBD and outperform current state-of-the-art methods
MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos
Visual data collected from Unmanned Aerial Vehicles (UAVs) has opened a new
frontier of computer vision that requires automated analysis of aerial
images/videos. However, the existing UAV datasets primarily focus on object
detection. An object detector does not differentiate between the moving and
non-moving objects. Given a real-time UAV video stream, how can we both
localize and classify the moving objects, i.e. perform moving object
recognition (MOR)? The MOR is one of the essential tasks to support various UAV
vision-based applications including aerial surveillance, search and rescue,
event recognition, urban and rural scene understanding.To the best of our
knowledge, no labeled dataset is available for MOR evaluation in UAV videos.
Therefore, in this paper, we introduce MOR-UAV, a large-scale video dataset for
MOR in aerial videos. We achieve this by labeling axis-aligned bounding boxes
for moving objects which requires less computational resources than producing
pixel-level estimates. We annotate 89,783 moving object instances collected
from 30 UAV videos, consisting of 10,948 frames in various scenarios such as
weather conditions, occlusion, changing flying altitude and multiple camera
views. We assigned the labels for two categories of vehicles (car and heavy
vehicle). Furthermore, we propose a deep unified framework MOR-UAVNet for MOR
in UAV videos. Since, this is a first attempt for MOR in UAV videos, we present
16 baseline results based on the proposed framework over the MOR-UAV dataset
through quantitative and qualitative experiments. We also analyze the
motion-salient regions in the network through multiple layer visualizations.
The MOR-UAVNet works online at inference as it requires only few past frames.
Moreover, it doesn't require predefined target initialization from user.
Experiments also demonstrate that the MOR-UAV dataset is quite challenging
Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery
A robust and fast automatic moving object detection and tracking system is
essential to characterize target object and extract spatial and temporal
information for different functionalities including video surveillance systems,
urban traffic monitoring and navigation, robotic. In this dissertation, I
present a collaborative Spatial Pyramid Context-aware moving object detection
and Tracking system. The proposed visual tracker is composed of one master
tracker that usually relies on visual object features and two auxiliary
trackers based on object temporal motion information that will be called
dynamically to assist master tracker. SPCT utilizes image spatial context at
different level to make the video tracking system resistant to occlusion,
background noise and improve target localization accuracy and robustness. We
chose a pre-selected seven-channel complementary features including RGB color,
intensity and spatial pyramid of HoG to encode object color, shape and spatial
layout information. We exploit integral histogram as building block to meet the
demands of real-time performance. A novel fast algorithm is presented to
accurately evaluate spatially weighted local histograms in constant time
complexity using an extension of the integral histogram method. Different
techniques are explored to efficiently compute integral histogram on GPU
architecture and applied for fast spatio-temporal median computations and 3D
face reconstruction texturing. We proposed a multi-component framework based on
semantic fusion of motion information with projected building footprint map to
significantly reduce the false alarm rate in urban scenes with many tall
structures. The experiments on extensive VOTC2016 benchmark dataset and aerial
video confirm that combining complementary tracking cues in an intelligent
fusion framework enables persistent tracking for Full Motion Video and Wide
Aerial Motion Imagery.Comment: PhD Dissertation (162 pages
Automated video processing and scene understanding for intelligent video surveillance
Title from PDF of title page (University of Missouri--Columbia, viewed on December 7, 2010).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Zhihai He.Vita.Ph. D. University of Missouri--Columbia 2010.Recent advances in key technologies have enabled the deployment of surveillance video cameras on various platforms. There is an urgent need to develop advanced computational methods and tools for automated video processing and scene understanding to support various applications. In this dissertation, we concentrate our efforts on the following four tightly coupled tasks: Aerial video registration and moving object detection. We develop a fast and reliable global camera motion estimation and video registration for aerial video surveillance. 3-D change detection from moving cameras. Based on multi-scale pattern, we construct a hierarchy of image patch descriptors and detect changes in the video scene using multi-scale information fusion. Cross-view building matching and retrieval from aerial surveillance videos. Identifying and matching buildings between camera views is our central idea. We construct a semantically rich sketch-based representation for buildings which is invariant under large scale and perspective changes. Collaborative video compression for UAV surveillance network. Based on distributed video coding, we develop a collaborative video compression scheme for a UAV surveillance network. Our extensive experimental results demonstrate that the developed suite of tools for automated video processing and scene understanding are efficient and promising for surveillance applications.Includes bibliographical reference
ClusterNet: Detecting Small Objects in Large Scenes by Exploiting Spatio-Temporal Information
Object detection in wide area motion imagery (WAMI) has drawn the attention
of the computer vision research community for a number of years. WAMI proposes
a number of unique challenges including extremely small object sizes, both
sparse and densely-packed objects, and extremely large search spaces (large
video frames). Nearly all state-of-the-art methods in WAMI object detection
report that appearance-based classifiers fail in this challenging data and
instead rely almost entirely on motion information in the form of background
subtraction or frame-differencing. In this work, we experimentally verify the
failure of appearance-based classifiers in WAMI, such as Faster R-CNN and a
heatmap-based fully convolutional neural network (CNN), and propose a novel
two-stage spatio-temporal CNN which effectively and efficiently combines both
appearance and motion information to significantly surpass the state-of-the-art
in WAMI object detection. To reduce the large search space, the first stage
(ClusterNet) takes in a set of extremely large video frames, combines the
motion and appearance information within the convolutional architecture, and
proposes regions of objects of interest (ROOBI). These ROOBI can contain from
one to clusters of several hundred objects due to the large video frame size
and varying object density in WAMI. The second stage (FoveaNet) then estimates
the centroid location of all objects in that given ROOBI simultaneously via
heatmap estimation. The proposed method exceeds state-of-the-art results on the
WPAFB 2009 dataset by 5-16% for moving objects and nearly 50% for stopped
objects, as well as being the first proposed method in wide area motion imagery
to detect completely stationary objects.Comment: Main paper is 8 pages. Supplemental section contains a walk-through
of our method (using a qualitative example) and qualitative results for WPAFB
2009 datase
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