DETECTING MULTI-OBJECT ANOMALIES UTILIZING A LOW RANK SPARSITY MODEL

摘要

Methods and systems for detecting anomalies in transportation related video footage. In an offline training phase, receiving video footage of a traffic location can be received. Also, in an offline training phase, event encodings can be extracted from the video footage and collected or compiled into a training dictionary. One or more input video sequences captured at the traffic location or a similar traffic location can be received in an online detection phase. Then, an event encoding corresponding to the input video sequence can be extracted. The event encoding can be reconstructed with a low rank sparsity prior model applied with respect to the training dictionary. The reconstruction error between actual and reconstructed event encodings can then be computed in order to determine if an event thereof is anomalous by comparing the reconstruction error with a threshold.

主权项

1. A method for detecting anomalies in transportation related video footage, said method comprising:
in an offline training phase, receiving video footage at a traffic location; in an offline training phase, extracting event encodings from said video footage 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.