Threat-detection in a distributed multi-camera surveillance system
First Claim
1. A distributed multi-camera surveillance system, comprising:
- a plurality of cameras including;
a first camera having a field of view and an associated data store for storing a trained statistical motion flow model for objects moving in the field of view of the first camera, the model based on observed motion vectors within the first camera'"'"'s field of view in combination with feedback data compiled and communicated peer-to-peer to the camera by another camera in response to a tracking request message issued by the first camera, the tracking request message communicating time stamp and flow identifier information associated with a trained statistical motion flow, and the feedback data received by said another camera communicating matching time stamp and corresponding flow identifier information that is used by the first camera to dynamically learn a topological relationship between the first camera and said another camera, wherein the motion flow model is a building algorithm embodied in software installed at each of the individual cameras of the plurality of cameras for constructing the motion flow model for their own independent fields of view and wherein the dynamic learning of the topological relationships between the cameras includes each individual camera dynamically updating its statistical model, in real-time, upon the receiving of the dynamically transmitted tracking requests transmitted by the other individual cameras, such that the individual cameras are able to dynamically and individually adapt to their individual orientations and positions;
the system further including;
the first camera operable to detect a threat when movement of an object in its field of view does not conform to the motion flow model and transmit a tracking request for the object over a network to a second camera; and
the second camera having a field of view and an associated data store for storing a motion flow model for objects moving in the field of view of the second camera, the second camera operable to detect a threat when movement of an object in its field of view does not conform to the motion flow model and generate an alarm based on the threat detected at the first and second cameras.
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Accused Products
Abstract
A method is provided for detecting a threat in a distributed multi-camera surveillance system. The method includes: monitoring movement of an object in a field of view of a first camera using software installed at the first camera; detecting a suspicious object at the first camera when movement of the object does not conform with a motion flow model residing at the first camera; sending a tracking request from the first camera to a second camera upon detecting the suspicious object at the first camera; monitoring movement of the object in a field of view of the second camera using software installed at the second camera; assigning threat scores at the second camera when the movement of the object does not conform with a motion flow model residing at the second camera; and generating an alarm based in part on the threat scores detected at the first camera and the second camera.
13 Citations
29 Claims
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1. A distributed multi-camera surveillance system, comprising:
- a plurality of cameras including;
a first camera having a field of view and an associated data store for storing a trained statistical motion flow model for objects moving in the field of view of the first camera, the model based on observed motion vectors within the first camera'"'"'s field of view in combination with feedback data compiled and communicated peer-to-peer to the camera by another camera in response to a tracking request message issued by the first camera, the tracking request message communicating time stamp and flow identifier information associated with a trained statistical motion flow, and the feedback data received by said another camera communicating matching time stamp and corresponding flow identifier information that is used by the first camera to dynamically learn a topological relationship between the first camera and said another camera, wherein the motion flow model is a building algorithm embodied in software installed at each of the individual cameras of the plurality of cameras for constructing the motion flow model for their own independent fields of view and wherein the dynamic learning of the topological relationships between the cameras includes each individual camera dynamically updating its statistical model, in real-time, upon the receiving of the dynamically transmitted tracking requests transmitted by the other individual cameras, such that the individual cameras are able to dynamically and individually adapt to their individual orientations and positions;
the system further including;
the first camera operable to detect a threat when movement of an object in its field of view does not conform to the motion flow model and transmit a tracking request for the object over a network to a second camera; and
the second camera having a field of view and an associated data store for storing a motion flow model for objects moving in the field of view of the second camera, the second camera operable to detect a threat when movement of an object in its field of view does not conform to the motion flow model and generate an alarm based on the threat detected at the first and second cameras. - View Dependent Claims (2, 3, 4, 5, 6, 28, 29)
- a plurality of cameras including;
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7. A method for detecting a threat in a distributed multi-camera surveillance system, comprising:
- a plurality of cameras including;
training a statistical motion flow model of a first camera based on observed motion vectors within the first camera'"'"'s field of view in combination with feedback data compiled and communicated peer-to-peer to the camera by another camera in response to a tracking request message issued by the first camera, the tracking request message communicating time stamp and flow identifier information associated with a trained statistical motion flow, and the feedback data received by said another camera communicating matching time stamp and corresponding flow identifier information that is used by the first camera to dynamically learn a topological relationship between the first camera and said another camera;
wherein the motion flow model is a building algorithm embodied in software installed at each of the individual cameras of the plurality of cameras for constructing the motion flow model for their own independent fields of view and wherein the dynamic learning of the topological relationships between the cameras includes each individual camera dynamically updating its statistical model, in real-time, upon the receiving of the dynamically transmitted tracking requests transmitted by the other individual cameras, such that the individual cameras are able to dynamically and individually adapt to their individual orientations and positions;
the system further including;
monitoring movement of an object in a field of view of the first camera using software installed at the first camera that accesses the statistical motion flow model of the first camera;
detecting a threat at the first camera when movement of the object does not conform with a motion flow model residing at the first camera;
using said tracking request message for the object from the first camera to a second camera upon detecting the threat at the first camera;
monitoring movement of the object in a field of view of the second camera using software installed at the second camera;
detecting a threat at the second camera when the movement of the object does not conform with a motion flow model residing at the second camera; and
generating an alarm based in part on the threat detected at the first camera and the second camera. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14)
- a plurality of cameras including;
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15. A method for learning motion flow of objects amongst two or more cameras in a distributed multi-camera surveillance system, comprising:
- training a statistical motion flow model of a first camera based on observed motion vectors within the first camera'"'"'s field of view in combination with feedback data compiled and communicated peer-to-peer to the camera by another camera in response to a tracking request message issued by the first camera, the tracking request message communicating time stamp and flow identifier information associated with a trained statistical motion flow, and the feedback data received by said another camera communicating matching time stamp and corresponding flow identifier information that is used by the first camera to dynamically learn a topological relationship between the first camera and said another camera;
detecting an object moving in a field of view of a first camera using software installed at the first camera that accesses the statistical motion flow model of the first camera;
wherein the motion flow model is a building algorithm embodied in software installed at each of the individual cameras of the plurality of cameras for constructing the motion flow model for their own independent fields of view and wherein the dynamic learning of the topological relationships between the cameras includes each individual camera dynamically updating its statistical model, in real-time, upon the receiving of the dynamically transmitted tracking requests transmitted by the other individual cameras, such that the individual cameras are able to dynamically and individually adapt to their individual orientations and positions;
the system further including;
using said tracking request message from the first camera across a network to other cameras in the network, wherein the tracking request provides an identifier for the first camera, an identifier for the object and visual attributes associated with the object;
searching for the object in a field of view of a second camera in response to the tracking request received from the first camera; and
associating the second camera with the first camera when the object is detected in the field of view of the second camera. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27)
- training a statistical motion flow model of a first camera based on observed motion vectors within the first camera'"'"'s field of view in combination with feedback data compiled and communicated peer-to-peer to the camera by another camera in response to a tracking request message issued by the first camera, the tracking request message communicating time stamp and flow identifier information associated with a trained statistical motion flow, and the feedback data received by said another camera communicating matching time stamp and corresponding flow identifier information that is used by the first camera to dynamically learn a topological relationship between the first camera and said another camera;
Specification