Carried Object Detection Based on an Ensemble of Contour Exemplars

We study the challenging problem of detecting carried objects (CO) in surveillance videos. For this purpose, we formulate CO detection in terms of determining a person's contour hypothesis and detecting CO by exploiting the remaining contours. A hypothesis mask for a person's contours is generated based on an ensemble of contour exemplars of humans with different standing and walking poses. Contours that are not falling in a person's contour hypothesis mask are considered as candidates for CO contours. Then, a region is assigned to each CO candidate contour using biased normalized cut and is scored by a weighted function of its overlap with the person's contour hypothesis mask and segmented foreground. To detect COs from obtained candidate regions, a non-maximum suppression method is applied to eliminate the low score candidates. We detect COs without protrusion assumption from a normal silhouette as well as without any prior information about the COs. Experimental results show that our method outperforms state-of-the-art methods even if we are using fewer assumptions.