Object retrieval in video data using complementary detectors

A system, comprising:

• a processing unit in communication with a computer readable memory and a tangible computer-readable storage medium;
• wherein the processing unit, when executing program instructions stored on the tangible computer-readable storage medium via the computer readable memory:
• creates a plurality of complementary detectors for each of a plurality of different motionlet clusters that are partitioned from a plurality of training dataset vehicle images as a function of determining that vehicles within each of the scenes of the images in each cluster share similar two-dimensional motion direction attributes within their scene, by:
• training a first detector on motion blobs of vehicle objects detected and collected within each of the training dataset vehicle images within the motionlet cluster via a background modeling process;
• training a second detector on each of the training dataset vehicle images within the motionlet clusters that have motion blobs of the vehicle objects but are misclassified by the first detector;
• and
• repeating the training the first and the second detector until all of the training dataset vehicle images within the motionlet cluster have been eliminated as false positives or correctly classified by the first or the second detector.

A computer program product, comprising:

• a computer readable hardware storage device having computer readable program code embodied therewith, the computer readable program code comprising instructions for execution by a computer processing unit that cause the computer processing unit to:
• create a plurality of complementary detectors for each of a plurality of different motionlet clusters that are partitioned from a plurality of training dataset vehicle images as a function of determining that vehicles within each of the scenes of the images in each cluster share similar two-dimensional motion direction attributes within their scene, by:
• training a first detector on motion blobs of vehicle objects detected and collected within each of the training dataset vehicle images within the motionlet cluster via a background modeling process;
• training a second detector on each of the training dataset vehicle images within the motionlet clusters that have motion blobs of the vehicle objects but are misclassified by the first detector;
• and
• repeating the training the first and the second detector until all of the training dataset vehicle images within the motionlet cluster have been eliminated as false positives or correctly classified by the first or the second detector.