Carried Object Detection using a Generic Shape Model and Positional Consistency
Aryana Tavanai, Muralikrishna Sridhar, Feng Gu, Anthony G. Cohn and David C. Hogg
This work applies local and global constraints in an optimisation procedure where the interpretation of the tracks (local constraints) and events (global constraints) mutually influence each other.- We build a model in terms of the person-object relationship over time which focuses on the carry event.
- Closed contours which are approximately convex are detected as potential carried objects and form a set of initial object detections.
- A high level interpretation of which objects are carried when and by whom is computed from high confidence object detections.
- The current high level interpretation induces a set of object tracks.
- Confidence estimates on object detections are changed based on the current object tracks.
- The high level interpretation from above is repeated until convergence.
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Software Download
Geometric carried object detection code will be available soon. (Please contact Aryana Tavanai for any queries)Tracking and optimisation code will be available in the near future.
Publications
| Aryana Tavanai, Muralikrishna Sridhar, Feng Gu, Anthony G. Cohn, and David C. Hogg. Carried Object Detection and Tracking using Geometric Shape Models and Spatio-Temporal Consistency. 9th International Conference on Computer Vision Systems, ICVS 2013. (To appear) |
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Datasets
The following two datasets have been used to evaluate the proposed approach.MINDSEYE2012: A subset of Mind's Eye Year 2 videos were selected.
PETS2006: All seven videos of the third camera were chosen from the PETS2006 dataset.