JAIST Multi-View Surveillance (MVS) Video Database
| From October 2010, a new project is launched to study the autonomous report generation for multiple view surveillance environment.
This research targets at efficient management and retrieval of media data in future large-scale surveillance system. Especially, we aim at developing a system to automatically
generate an online/offline field reports to satisfy various user preferences, based on contextual informations.
*This research is supported by the 2010 Research Grant-in-Aid (Start-up Support).
In this page, we present a database, namely the JAIST-MVS database, which collects the video data that we need to study people tracking, abnormality detection and report generation.
Public DistributionWith the permission of the life-science commitee, we are able to make the data available to the researchers in computer vision community, after signing a user agreement. We will only provide the low resolution version of the database, which will be available through network downloading. The high quality version and all other necessary information may be accessed by sending an Email to Dr Fan CHEN at chen-fan AT jaist.ac.jp , giving the names of the researchers who wish to use the data and their main purposes.
Acquisition SetupThis video database was acquired by eight high definition surveillance cameras (Sanyo VCC-HD2300 with wide-angle lens Fujinon YV2.8x2.8SA-2) that were setup in our lab room.
All videos were taken in a rectangle lab room, where the action zone is around 3.5m x 4.5 m.
Here is an image to provide an initial idea of each camera view and its effictive area.
Action ListsWith the permission of the life-science commitee, we organized one video acquisition activity. All videos were recorded at around 30FPS. Since the cameras accept no external trigger signals, all videos were only software synchronized. The users may need to adjust the time offset of each video. In this acquisition, we include three groups of video data into the JAIST-MVS database:
1. Single actionsIn this subset, we ask each person to perform an action in the middle of the room. We collect eight types of single-person actions from five different persons.
A0S: Normal walkingEnter the room, walk for two rounds and exit the room.
A1S: Drunkard walkingEnter the room, drunkard walk for two rounds and exit the room.
A2S: Sneak walkingEnter the room, sneak walk for two rounds and exit the room.
A3S: Object taking/returningEnter the room, take a bag away from the central table, and then exit the room.
Enter the room again and put the bag back.
A4S: Object smashing with a hammerAct as crushing an unreal object in the middle of the room, with a hammer.
A5S: Peek into a carAssume that there is a car and peek into the window。
A6S: Falling downAct as a patient, who slowly falls down due to heart attack.
A7S: Lock pickingAssume that there is a door in the middle of the room. Stop by in the middle of the room, place a knee on the mattress, and take out an electrical driver.
2. Group actionsIn this subset, we ask several persons to perform an action in the middle of the room. We collect eight types of actions.
A0G: Group walkingMore persons are walking freely, with leaving and entering the room
A1G: Group walking with interactionMore people are walking freely. Time by time, two persons walk closer, have a short chat, and then separate.
A2G: Object droppingThe first one drops something, and the second one picks it up and leaves. Other people keep walking.
A3G: Bag exchangeTwo persons meet each other and exchange their bags/suitcases. Other people keep walking.
A4G: Bag stealingThe first one leaves his suitcase for a while, and the second one takes it away. Other people keep walking.
A5G: FightingTwo people start to fight and all other people stop by and circulate these two persons.
A6G: Falling down due to external forcesA push B, which makes B fall down. A leaves the place quickly. Other people start to check B's situation. B stands up. All persons continue normal walking.
A7G: Falling down due to internal reasonPerson A falls down, and Person B comes closer to Person A for checking the situation and then start to call other people. Other people start to circulate them.
3. Long videos of free combination of actions.In this subset, we ask several persons to perform a free combination of actions. According to the density of abnormal events, we collected three videos.
Overall 0: High abnormal event density
Ground-truth data of people tracking from manual labelling. [Updated Jul. 23, 2011]
Overall 1: Medium abnormal event density(Thumbnail Omitted)
Overall 2: Sparse abnormal event density(Thumbnail Omitted)
4. Some People Tracking Results. [Updated Jun. 14, 2011]We show here some people tracking results based on the above database.
Results on the video of Overall 0: High abnormal event density
Results on the video of Overall 1: Medium abnormal event density
Results on the video of Overall 2: Sparse abnormal event density
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