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Intelligent Highway Traffic Surveillance With Self-Diagnosis Abilities
IntelligentHighwayTrafficSurveillanceWithSelfDiagnosisAbilities.pdf
BGSLibrary
See BGSLibrary with 34 background subtraction algorithm:
https://github.com/andrewssobral/bgslibrary
ViBe
Occlusion detection using horizontally segmented windows for vehicle tracking
OcclusionDetectionUsingHorizontallySegmentedWindowsForVehicleTracking.pdf
A Moving Objects Tracking Method Based on a Combination of Local Binary Pattern Texture and Hue
AMovingObjectsTrackingMethodBasedOnACombinationOfLocalBinaryPatternTextureAndHue.pdf
Vehicle Tracking Using Feature Matching and Kalman Filtering
VehicleTrackingUsingFeatureMatchingAndKalmanFiltering.pdf
SIFT + KF for vehicle tracking!
3D Model Based Vehicle Tracking Using Gradient Based Fitness Evaluation Under Particle Filter Framework
3DModelBasedVehicleTrackingUsingGradientBasedFitnessEvaluationUnderParticleFilterFramework.pdf
On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation
OnRoadMultivehicleTrackingUsingDeformableObjectModelAndParticleFilterWithImprovedLikelihoodEstimation.pdf
PhDThesis: Stochastic Real-Time Urban Traffic State Estimation: Searching for the
PhD_StochasticRealTimeUrbanTrafficStateEstimation_SearchingForTheMostLikelyHypothesisWithLimitedAndHeterogeneousSensorData.pdf
Vehicle Detection using Haar Cascades
Basic Vehicle Detection using Haar Cascades:
https://www.youtube.com/watch?v=c4LobbqeKZc
A feature-based tracking algorithm for vehicles in intersections
AFeatureBasedTrackingAlgorithmForVehiclesInIntersections.pdf
AFeatureBasedTrackingAlgorithmForVehiclesInIntersections_2.pdf
Enhancement of Particle Filter Approach for Vehicle Tracking via Adaptive Resampling Algorithm
EnhancementOfParticleFilterApproachForVehicleTrackingViaAdaptiveResamplingAlgorithm.pdf
Efficient Feature Extraction and Likelihood Fusion for Vehicle
EfficientFeatureExtractionAndLikelihoodFusionForVehicleTrackingInLowFrameRateAirborneVideo.pdf
OpenCV Tutorial - Optic Flow, Kalman Filter
Beyond real-time video surveillance with GPU accelerated Panoptes
BeyondRealtimeVideoSurveillancewithGPUAcceleratedPanoptes.pdf
Robust Visual Tracking and Vehicle Classification via Sparse Representation
RobustVisualTrackingAndVehicleClassificationViaSparseRepresentation.pdf
Background Models Challenge
Competition for the comparison of background subtraction algorithms:
http://web.archive.org/web/20130530160059/http://bmc.univ-bpclermont.fr/
Image Moments
cvb::CvBlob already has image moment related attributes.
HU's invariant moments:
https://www.youtube.com/watch?v=O-hCEXi3ymU
Machine Vision Using Image Moments:
https://www.youtube.com/watch?v=Nc06tlZAv_Q
Hierarchical Kalman-particle filter with adaptation to motion changes for object tracking
HierarchicalKalmanParticleFilterWithAdaptationToMotionChangesForObjectTracking.pdf
OpenCV Demo: Object tracking with Kalman Filter
https://www.youtube.com/watch?v=sG-h5ONsj9s
A video that demonstrates the use of Kalman filter to track the movements of a blue ball even when occlusions occur.
Green rectangle is the true measure, red box is the state estimation by Kalman Filter.
Follow my work and my tutorials on http://www.robot-home.it/blog/?lang=en
Foreground Object Detection from Videos Containing Complex Background
ForegroundObjectDetectionFromVideosContainingComplexBackground.pdf
Multi-cue-based CamShift guided particle filter tracking
MultiCueBasedCamShiftGuidedParticleFilterTracking.pdf
Monocular Visual Scene Understanding: Understanding Multi-Object Traffic Scenes
MonocularVisualSceneUnderstanding_UnderstandingMultiObjectTrafficScenes.pdf
Real-time Multiple Vehicles Tracking with Occlusion Handling
RealTimeMultipleVehiclesTrackingWithOcclusionHandling.pdf
Vehicle Detection and Traffic Assessment Using Images
VehicleDetectionAndTrafficAssessmentUsingImages.pdf
PGH: Pairwise Geometrical Histogram
A Relative-Discriminative-Histogram-of-Oriented-Gradients-Based Particle Filter Approach to Vehicle Occlusion Handling and Tracking
ARelativeDiscriminativeHistogramOfOrientedGradientsBasedParticleFilterApproachToVehicleOcclusionHandlingAndTracking.pdf
Video Processing Techniques for Traffic Flow Monitoring: A Survey
VideoProcessingTechniquesForTrafficFlowMonitoring_ASurvey.pdf
Attribute-based Vehicle Search in Crowded Surveillance Videos
AttributeBasedVehicleSearchInCrowdedSurveillanceVideos.pdf
Overlapping Vehicle Tracking via Adaptive Particle Filter with Multiple Cues
OverlappingVehicleTrackingViaAdaptiveParticleFilterWithMultipleCues.pdf
Layered graphical models for tracking partially-occluded objects
LayeredGraphicalModelsForTrackingPartiallyOccludedObjects.pdf
Multiframe Many–Many Point Correspondence for Vehicle Tracking in High Density Wide Area Aerial Videos
MultiframeManyManyPointCorrespondenceForVehicleTrackingInHighDensityWideAreaAerialVideos.pdf
Visual tracking using the Earth Mover's Distance between Gaussian mixtures and Kalman filtering
VisualtrackingUsingTheEarthMoversDistanceBetweenGaussianMixturesAndKalmanFiltering.pdf
ViNotion object detection from moving vehicle (car detection) (proprietary algorithm)
Digital Radar Traffic Classifier
CountingTrafficUsingRadar.pdf
http://www.camea.cz/en/traffic-applications/traffic-monitoring/digital-radar-traffic-classifier/
Change Detection Benchmark site
Change detection academic benchmarking site:
video dataset:
http://wordpress-jodoin.dmi.usherb.ca/dataset2014/
results:
http://wordpress-jodoin.dmi.usherb.ca/results2014/
Vehicle Detection, Tracking and Classification in Urban Traffic
VehicleDetectionTrackingAndClassificationInUrbanTraffic.pdf
Real-time Tracking of Low-resolution Vehicles for Wide-Area Persistent Surveillance
RealTimeTrackingOfLowResolutionVehiclesForWideAreaPersistentSurveillance.pdf
"Once the reports from the detection module are in LL-
space, we use a multi-hypothesis tracker with a Kalman
filter position/velocity motion model [5]. Tracks are rep-
resented as hypothesis trees with maximum depths set to
2."
A Review of Computer Vision Techniques for the Analysis of Urban Traffic
AReviewOfComputerVisionTechniquesForTheAnalysisOfUrbanTraffic.pdf
A Particle-Filtering Approach for Vehicular Tracking Adaptive to Occlusions
AParticleFilteringApproachForVehicularTrackingAdaptiveToOcclusions.pdf
Part-based Multiple-Person Tracking with Partial Occlusion Handling
PartBasedMultiplePersonTrackingWithPartialOcclusionHandling.pdf
RMotion with additional neural networks
Vehicle classification through multiple neural networks.
RMotion with additional neural networks
http://rikiji.it/2012/06/26/RMotion-with-additional-neural-networks.html
https://github.com/rikiji/rmotion
Recognize moving objects on webcam video streams through a ruby interface. New neural-network capable version available soon.
A New Approach for Adaptive Background Object Tracking Based on Kalman Filter and Mean Shift
ANewApproachForAdaptiveBackgroundObjectTrackingBasedOnKalmanFilterAndMeanShift.pdf
Comparative Study of Background Subtraction Algorithms
ComparativeStudyOfBackgroundSubtractionAlgorithms.pdf
Low-Rank Sparse Learning for Robust Visual Tracking
LowRankSparseLearningForRobustVisualTracking.pdf
Google Site - Background Subtraction
See the wast amount of algorithms collected here:
https://sites.google.com/site/backgroundsubtraction/Home
Three-Dimensional Deformable-Model-Based Localization and Recognition of Road Vehicles
ThreeDimensionalDeformableModelBasedLocalizationAndRecognitionOfRoadVehicles.pdf
Comments about the tracking algorithm in cvblob (Blob library for OpenCV)
http://code.google.com/p/cvblob/wiki/FAQ#1.4_What%27s_wrong_with_the_tracking_algorithm
"1.4 What's wrong with the tracking algorithm?
In older versions of cvBlob the tracking was unusable. It had bugs and the algorithm needed to be fixed.
The tracking code was rewritten in version 0.9.13 and now it works a lot better, but there is some bugs yet.
Keep in mind that the tracking algorithm in cvBlob is very simple. It's only for testing or fast prototyping.
1.5 What is the tracking algorithm that cvBlob uses?
In the beginning, my tracking algorithm was based on the high level tracking of the paper of A. Senior, A. Hampapur, Y-L Tian, L. Brown, S. Pankanti and R. Bolle called "Appearance Models for Occlusion Handling".
But then I rewrote it again and again.
As I said before, the tracking algorithm it's only for fast prototyping. It is not intended for serious projects. cvBlob's tracking algorithm only takes into account the position and bounding box of the blobs. But a good tracking algorithm need to consider:
The history of the positions of the blob (actually this could be added to cvBlob, as a Kalman filter, or something like that... maybe in the future).
Appearance models (this is outside the bounds of the project): this can be color histograms, shape features,...
This two points depends on the problem that you need to approach, and it's difficult to give a general solution."
OpenCV documentation - Motion Analysis and Object Tracking
Real-Time Multi-Vehicle Tracking Based on Feature Detection and Color Probability Model
RealTimeMultiVehicleTrackingBasedOnFeatureDetectionAndColorProbabilityModel.pdf
Acoustic Traffic Classification using an Artificial Neural Network
AcousticTrafficClassificationUsingAnArtificialNeuralNetwork.pdf
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