Detecting multi-object anomalies utilizing a low rank sparsity model

A system for detecting anomalies in transportation related video footage, said system comprising:

• a processor;
• and
• a non-transitory computer-usable medium embodying computer program code, said non-transitory computer-usable medium capable of communicating with said processor, said computer program code comprising instructions executable by said processor and configured for:
• in an offline training phase, receiving video footage at a traffic location;
• in an offline training phase, extracting event encodings from said video footage received at said traffic location and collecting said event encodings into a training dictionary;
• in an online detection phase, receiving at least one input video sequence captured at said traffic location or a similar traffic location;
• extracting an event encoding corresponding to said at least one input video sequence;
• reconstructing said event encoding with a low rank sparsity prior model applied with respect to said training dictionary;
• and
• computing a reconstruction error between actual and reconstructed event encodings in order to determine if an event thereof is anomalous by comparing said reconstruction error with a threshold, wherein said computing said reconstruction error between said actual and reconstructed event encodings and determining if said event is anomalous includes asserting that said event is normal if the following test holds true:
• 
• y
• i
• -
• y
• ^
• i
• 
• 2
• 
• y
• i
• 
• 2
• <
• τ
• →
• y
• i
• ⁢
• wherein said y comprises a true event encoding, ŷi comprises a reconstructed event encoding, and τ comprises a predetermined threshold.

A non-transitory processor-readable medium storing computer code representing instructions to cause a process for detecting anomalies in transportation related video footage, said computer code further comprising code to:

• in an offline training phase, receive video footage at a traffic location;
• in an offline training phase, extract event encodings from said video footage received at said traffic location and collecting said event encodings into a training dictionary;
• in an online detection phase, receive at least one input video sequence captured at said traffic location or a similar traffic location;
• extract an event encoding corresponding to said at least one input video sequence;
• reconstruct said event encoding with a low rank sparsity prior model applied with respect to said training dictionary;
• and
• compute a reconstruction error between actual and reconstructed event encodings in order to determine if an event thereof is anomalous by comparing said reconstruction error with a threshold, wherein said computing said reconstruction error between said actual and reconstructed event encodings and determining if said event is anomalous includes asserting that said event is normal if the following test holds true:
• 
• y
• i
• -
• y
• ^
• i
• 
• 2
• 
• y
• i
• 
• 2
• <
• τ
• ->
• y
• i
• wherein said y comprises a true event encoding, ŷi comprises a reconstructed event encoding, and r comprises a predetermined threshold.