1. BEHAVE - INTERACTION
Website: http://groups.inf.ed.ac.uk/vision/BEHAVEDATA/INTERACTIONS/
Datasets: The dataset comprises of two views of various scenario's of people acting out various interactions. Ten basic scenarios were acted out. These were called InGroup (IG), Approach (A), WalkTogether (WT), Split (S), Ignore (I), Following (FO), Chase (C), Fight (FI), RunTogether (RT), and Meet (M).The data is captured at 25 frames per second. The resolution is 640x480. The videos are available either as AVI's or as a numbered set of JPEG single image files.
Metadata:
Tracking, Event detection.
Contextual info:
3D coordinates of points for calibration purposes provided.
Comments:
The site will be updated when more of the ground truth becomes available.
Copyrights:
Free download from website.
Contact:
Dimitrios Makris, [email protected]
2. ETISEO - Surveillance
Website: http://www-sop.inria.fr/orion/ETISEO/ (registration is needed)
Dataset: 86 video clips. These sequences constitute a representative panel of different video surveillance areas.
They merge indoor and outdoor scenes, corridors, streets, building entries, subway station... They also mix different types of sensors and complexity levels.
Metadata:
5 different levels: Object Detection, Object Localization, Object Tracking, Object Classification.
Contextual info:
Zone of interest, calibration matrix
Comments:
Copyrights:
Free download but registration and user agreement is required.
Contact:
3. CANDELA - Surveillance
Website: http://www.multitel.be/~va/candela/
Dataset: Two different scenarios have been relaized during the CANDELA project : "Indoor abandonned object" and "road intersection".
o Scenario 1: Abandoned object. The detection of abandoned objects is more or less the detection of idle (stationary or non-moving) objects that remain stationary over a certain period of time. The period of time is adjustable. In several types of scenes, idle objects should be detected. In a parking lot e.g., an idle object can be a parked car or a left suitcase. For this scenario we are not looking at the object types "person" or "car", but at unidentified objects, called "unknown objects". An unknown object is any object that is not a person or a vehicle. In general, unknown objects cannot move. What should be detected? : Whenever an unknown object appears in the scene and remains stationary for some amount of time person, an alarm needs to be generated. This alarm must remain active, as long as the unknown object remains stationary.
o Scenario 2: Persons are allowed to cross the street at zebra crossings, a crossing controlled with lights. Alarms should be generated when persons are not allowed to be on the crossing, or when dangerous scenarios occur (cars driving when people crossing). Since the external signal from the traffic light is not available (when the crossing is regulated by traffic lights), detection needs to be done automatically. Detection of persons on the crossing itself is pretty easy, but alarms should only be given when persons are on the crossing, and cars are driving.
Metadata:
Detailed information about data and metadatas can be found here:
http://www.hitech-projects.com/euprojects/candela/pr/scenario_description_document_v06.pdf
Contextual info:
Comments:
Copyrights:
Public domain
Contact:
Xavier Desurmont, [email protected]
4.VISOR - Surveillance
Website: http://imagelab.ing.unimore.it/visor/
Dataset:
4 types of video clips. These sequences constitute a representative panel of different video surveillance areas.
They merge indoor and outdoor scenes, such as Indoor Domotic Unimore D.I.I. setup.
Metadata:
Object Detection and Tracking.
Contextual info:
Comments:
Mostly simple videos.
Copyrights:
Free download
Contact:
5. BEHAVE - Crowds
Website: http://groups.inf.ed.ac.uk/vision/BEHAVEDATA/CROWDS/index.html
Dataset:
Data for the real scene:
These are the smoothed flow sequences for the Waverly train station scene. There are 4 files number. (002) is used for testing, the remaining used for training.
Data for the simulated scene
These are the smoothed flow sequences for the train station simulation. There are 30 files divided in the groups below. Use from frame 1100 to 4000. The emergency is at frame 2000.
Group 1: Normal - Training
Group 2: Normal - Testing
Group 3: Emergency - Blocked exit at the bottom of the scene.
Metadata:
No Ground Thruth available
Contextual info:
Comments:
Copyrights:
Free download from website.
Contact:
Dimitrios Makris, [email protected]
6. PETS - 2007 - REASON
Website: http://www.pets2007.net/
Dataset: The datasets are multisensor sequences containing the following 3 scenarios, with increasing scene complexity: 1. loitering, 2. attended luggage removal (theft), 3. unattended luggage.
Metadata:
Event Detection
Contextual info:
Calibration provided
Comments:
Free download from website . The UK Information Commisioner has agreed that the PETS 2007 datasets described here may be made publicly available for the purposes of academic research. The video sequences are copyright UK EPSRC REASON Project consortium and permission is hereby granted for free download for the purposes of the PETS 2007 workshop.
Copyrights:
Contact:
Dimitrios Makris, [email protected]
7. I-LIDS - Surveillance
Dataset: 4 scenarios (Parked Vehicle, Abandoned Package, Doorway Surveillance and Sterile Zone) x 2 datasets (training, testing) each. Each dataset contains about 24 hours of footage in few different scenes.
Metadata:
Event-based Ground truth.
Contextual info:
Images of a pedestrian model in different positions are given for calibration purposes
Comments:
Copyrights:
A user agreement and a payment (£500-£650 per dataset) is required to obtain each dataset. Datasets are provided in hard disks.
Contact:
Dimitrios Makris, [email protected]
8. Actions as Space-Time Shapes
Website: http://www.wisdom.weizmann.ac.il/~vision/SpaceTimeActions.html
Dataset: Human action in video sequences can be seen as silhouettes of a moving torso and protruding limbs undergoing articulated motion. We regard human actions as three-dimensional shapes induced by the silhouettes in the space-time volume. We adopt a recent approach by Gorelick et. al. for analyzing 2D shapes and generalize it to deal with volumetric space-time action shapes. Our method utilizes properties of the solution to the Poisson equation to extract space-time features such as local space-time saliency, action dynamics, shape structure and orientation. We show that these features are useful for action recognition, detection and clustering. The method is fast, does not require video alignment and is applicable in (but not limited to) many scenarios where the background is known. Moreover, we demonstrate the robustness of our method to partial occlusions, non-rigid deformations, significant changes in scale and viewpoint, high irregularities in the performance of an action and low quality video.
Metadata:
Contextual info:
Comments:
Copyrights:
Contact:
9. KTH - Recognition of human actions
Website: http://www.nada.kth.se/cvap/actions/
Dataset: The current video database containing six types of human actions (walking, jogging, running, boxing, hand waving and hand clapping) performed several times by 25 subjects in four different scenarios: outdoors s1, outdoors with scale variation s2, outdoors with different clothes s3 and indoors s4 as illustrated below. Currently the database contains 2391 sequences. All sequences were taken over homogeneous backgrounds with a static camera with 25fps frame rate. The sequences were downsampled to the spatial resolution of160x120 pixels and have a length of four seconds in average.
Metadata:
Contextual info:
Comments:
Copyrights:
Contact:
laptev(at)nada.kth.se