Video data storage, search, and retrieval using meta-data and attribute data in a video surveillance system

Claim 3

The method of claim 1 , wherein the attribute weights are changed based on external events by computing future attribute weights from past attribute weights by composing past attribute weights with external event weights as shown in the equation: [ w 1 w 2 … w j ] = [ e 1 , e 2 , … ⁢ , e n ] · [ w 1 w 2 … w i ] , where w j are future attribute weights, w i are past attribute weights, and e n are external event weights.

Claim 4

The method of claim 1 , further comprising: receiving video tips from one or more anonymous sources, the video tips being short video clips captured by citizens; generating tip meta-data based on the video tips, the tip meta-data representing events detected in the video tips; and determining tip attribute weights for the tip meta-data, representing information about the relevance of the tip meta-data.

Claim 5

The method of claim 1 , further comprising: providing additional meta-data generated by a human operator; and storing the additional human generated meta-data, indexed to the video data by date and time stamp, in the meta-data storage module.

Claim 6

The method of claim 1 , further comprising: retrieving historical meta-data from the meta-data storage module; evaluating a set of rules based on the historical meta-data and the generated meta-data; and performing one or more actions based on the evaluation of the set of rules.

Claim 7

The method of claim 1 , wherein the video storage module is a hierarchical storage module that archives the video data based at least on meta-data and attribute weights associated with the video data.

Claim 8

The method of claim 1 , further comprising: storing access privileges for the video data, the meta-data, and the attribute weights.

Claim 9

The method of claim 1 , further comprising: encrypting the captured video data before storing the video data.

Claim 10

The method of claim 1 , wherein the video data is stored off-site.

Claim 11

A video surveillance system, comprising: one or more surveillance cameras for capturing video data; one or more video storage areas for storing video data; a meta-data storage area for storing meta-data; an attribute storage area for storing attribute weights; and a processor, the processor coupled to the video storage areas, the meta-data storage area, and the attribute storage area, the processor adapted to execute program code to: capture video data from one or more surveillance cameras; generate meta-data by performing video analysis on the video data from the surveillance cameras, the meta-data representing events detected in the video data; determine attribute weights, representing information about the relevance of the meta-data; generate intersections of two or more subsets of the meta-data to generate intersection meta-data; determine attribute weights associated with the intersection meta-data by multiplying the attribute weights for each subset of meta-data; generate unions of two or more subsets of the meta-data to generate union meta-data; determine attribute weights associated with the union meta-data by adding the attribute weights for each subset of meta-data and subtracting a multiple of the attribute weights of each subset of meta-data; change the attribute weights based on external events by computing future attribute weights from past attribute weights by composing past attribute weights with external event weights; store the video data in a video storage area; store the meta-data, indexed by date and time stamp to the video data, in a meta-data storage area; and store the attribute weights in an attribute storage area, wherein attribute weights for the intersection meta-data is calculated using the equation: W ( M 1 ∩M 2 )= W ( M 1 )· W ( M 2 ), wherein attribute weights for the union meta-data is calculated using the equation: W ( M 1 ∪M 2 )= W ( M 1 )+ W ( M 2 )− W ( M 1 )· W ( M 2 ), and wherein M 1 and M 2 are two subsets of meta-data, W(M 1 ) is an attribute weight associated with subset M 1 , W(M 2 ) is an attribute weight associated with subset M 2 , W(M 1 ∩M 2 ) is a calculated attribute weight associated with the intersection meta-data of subset M 1 and subset M 2 , and W(M 1 ∪M 2 ) is a calculated attribute weight associated with the intersection meta-data of subset M 1 and subset M 2 .

Claim 12

A video surveillance system, comprising: one or more surveillance cameras for capturing video data; one or more video storage areas for storing video data; a meta-data storage area for storing meta-data; an attribute storage area for storing attribute weights; and a processor, the processor coupled to the video storage areas, the meta-data storage area, and the attribute storage area, the processor adapted to execute program code to: capture video data from one or more surveillance cameras; generate meta-data by performing video analysis on the video data from the surveillance cameras, the meta-data representing events detected in the video data; determine attribute weights, representing information about the relevance of the meta-data; generate intersections of two or more subsets of the meta-data to generate intersection meta-data; determine attribute weights associated with the intersection meta-data by multiplying the attribute weights for each subset of meta-data; generate unions of two or more subsets of the meta-data to generate union meta-data; determine attribute weights associated with the union meta-data by adding the attribute weights for each subset of meta-data and subtracting a multiple of the attribute weights of each subset of meta-data; change the attribute weights based on external events by computing future attribute weights from past attribute weights by composing past attribute weights with external event weights; store the video data in a video storage area; store the meta-data, indexed by date and time stamp to the video data, in a meta-data storage area; and store the attribute weights in an attribute storage area, wherein attribute weights for the intersection meta-data is calculated using the equation: W ( M 1 ∩M 2 )= W ( M 1 )· W ( M 2 ), wherein attribute weights for the union meta-data is calculated using the equation: W ( M 1 ∪M 2 )= W ( M 1 )+ W ( M 2 )− W ( M 1 )· W ( M 2 ), and wherein M 1 and M 2 are two subsets of meta-data, W(M 1 ) is an attribute weight associated with subset M 1 , W(M 2 ) is an attribute weight associated with subset M 2 , W(M 1 ∩M 2 ) is a calculated attribute weight associated with the intersection meta-data of subset M 1 and subset M 2 , and W(M 1 ∪M 2 ) is a calculated attribute weight associated with the intersection meta-data of subset M 1 and subset M 2 .

Claim 13

The apparatus of claim 10 , wherein the attribute weights are changed based on external events by computing future attribute weights from past attribute weights by composing past attribute weights with external event weights as shown in the equation: [ w 1 w 2 … w j ] = [ e 1 , e 2 , … ⁢ , e n ] · [ w 1 w 2 … w i ] , where w j are future attribute weights, w i are past attribute weights, and e n are external event weights.

Claim 14

The apparatus of claim 10 , wherein the processor further comprises program code to: receive video tips from one or more sources, the video tips being short video clips captured by citizens; generate tip meta-data based on the video tips; determine tip attribute weights for the tip meta-data; and store the video tips in the video storage areas.

Claim 15

The apparatus of claim 10 , wherein the processor further comprises program code to: provide additional meta-data generated by a human operator; and store the additional human generated meta-data, indexed to the video data by date and time stamp, in the meta-data storage module.

Claim 16

The apparatus of claim 10 , wherein the processor further comprises program code to: retrieve historical meta-data from the meta-data storage module; evaluate a set of rules based on the historical meta-data and the generated meta-data; and perform one or more actions based on the evaluation of the set of rules.

Claim 17

The apparatus of claim 10 , further comprising: a hierarchical video storage module adapted to archive the video data based at least on meta-data and attribute weights associated with the video data.

Claim 18

The apparatus of claim 10 , further comprising: a fiber optic line to an off-site location for archiving the video data off-site.

Claim 21

The method of claim 17 , wherein the attribute weights includes data about the source of the meta-data.

Claim 22

The method of claim 17 , further comprising: storing audit information about who and when retrieved the video data.

Claim 25

The apparatus of claim 20 , wherein the attribute weights are changed based on external events by computing future attribute weights from past attribute weights by composing past attribute weights with external event weights as shown in the equation: [ w 1 w 2 … w j ] = [ e 1 , e 2 , … ⁢ , e n ] · [ w 1 w 2 … w i ] , where w j are future attribute weights, w i are past attribute weights, and e n are external event weights.

Claim 26

The apparatus of claim 20 , further comprising: means for receiving video tips from one or more anonymous sources, the video tips being short video clips captured by citizens; means for generating tip meta-data based on the video tips, the tip meta-data representing events detected in the video tips; and means for determining tip attribute weights for the tip meta-data, representing information about the relevance of the tip meta-data.