Detection and classification of vehicles from omnidirectional videos using multiple silhouettes

@article{Karaimer2017DetectionAC,
  title={Detection and classification of vehicles from omnidirectional videos using multiple silhouettes},
  author={Hakki Can Karaimer and I. Baris Schlicht and Yalin Bastanlar},
  journal={Pattern Analysis and Applications},
  year={2017},
  volume={20},
  pages={893-905}
}
To detect and classify vehicles in omnidirectional videos, we propose an approach based on the shape (silhouette) of the moving object obtained by background subtraction. Different from other shape-based classification techniques, we exploit the information available in multiple frames of the video. We investigated two different approaches for this purpose. One is combining silhouettes extracted from a sequence of frames to create an average silhouette, the other is making individual decisions… 
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Methods Citations
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References

SHOWING 1-10 OF 29 REFERENCES
Detection and Classification of Vehicles From Video Using Multiple Time-Spatial Images
TLDR
Experimental results demonstrate that the proposed method provides a significant improvement in counting and classifying the vehicles in terms of accuracy and robustness alongside a substantial reduction of execution time, as compared with that of the other methods.
Detection and classification of vehicles
TLDR
Algorithm for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence is presented.
Vehicle type categorization: A comparison of classification schemes
TLDR
Research to classify road vehicles into a range of broad categories using simple measures of size and shape derived from view-dependent binary silhouettes using images derived from a static roadside CCTV camera is described.
Improved Vehicle Classification in Long Traffic Video by Cooperating Tracker and Classifier Modules
  • B. Morris, M. Trivedi
  • Computer Science
    2006 IEEE International Conference on Video and Signal Based Surveillance
  • 2006
TLDR
The benefit gained both in tracking and classification through the communication between these individual modules are demonstrated on a real-time system monitoring highway traffic.
Semi-automatic annotation samples for vehicle type classification in urban environments
TLDR
This study presents a semi-automatic approach for the annotation of the vehicle samples recorded from roadside CCTV video cameras, which significantly reduces the time required to generate an annotated dataset.
Framework for real-time behavior interpretation from traffic video
TLDR
A rule-based framework for behavior and activity detection in traffic videos obtained from stationary video cameras is presented and successful behavior recognition results for pedestrian-vehicle interaction and vehicle-checkpost interactions are demonstrated.
Detection and classification of vehicles for urban traffic scenes
TLDR
A vehicle detection and classification system for urban traffic scenes that aims to guide surveillance operators and reduce human resources for observing hundreds of cameras in urban traffic surveillance is presented.
Multi-camera Based Traffic Flow Characterization & Classification
We describe a system that employs the use of an omnidirectional camera in tandem with a pan-tilt-zoom (PTZ) camera in order to characterize traffic flows, analyze vehicles, and detect and capture…
Classification of Ships in Surveillance Video
TLDR
An empirical study on classifying 402 instances of ship regions into 6 types based on their shape features using MPEG-7 region-based shape descriptor and k nearest neighbor algorithm, which is robust to noise and imperfect object segmentation.
Video Based Surround Vehicle Detection, Classification and Logging from Moving Platforms: Issues and Approaches
TLDR
The issues and approaches involved in developing a mobile vehicle-mounted system to detect, classify, and log the surrounding vehicles in a database for efficient query-based retrieval are discussed.
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