1. We propose a successive convex matching method to detect actions in videos. The proposed scheme does not need foreground/background separation, works in strong clutter and cases in which objects have large deformation. The proposed scheme converts the hard non-convex problem into a sequence of much easier convex programs. Actions can be represented as a sequence of postures with temporal constraints. Action detection is carried out by posture sequence matching and template-target similarity comparison. Matching a template posture sequence to video is formulated as an optimization problem: The above problem is usually highly non-convex. If d(.) uses an L1 norm, we can relax it into linear programming (LP): Property 1. If matching cost surfaces are convex, the LP exactly solves the continuous extension of the discrete matching problem. Property 2. For general problems, the LP approximates the original cost surfaces with their lower convex hulls. Property 3. We need only several basis target points (corresponding to the lower convex hull vertices) to construct the LP. Property 4. Using Simplex Method there are at most 3 non- zero target points for each site. Successive Convex Matching for Action Detection Hao Jiang, Mark S. Drew and Ze-Nian Li School of Computing Science, Simon Fraser University, Vancouver BC, Canada V5A 1S6 Propose a successive convex programming scheme to match video sequences using intra-frame and inter-frame constrained local features. The proposed scheme involves a very small number of basis points and thus can be applied to problems that involve a large number of target points. It has been successfully applied to locating specific actions in video sequences. Fig. 2. Template to video sequence matching. Fig. 3 Cost surface, lower convex hull and basis labels. For further information Please contact {hjiangb, mark, li}@cs.sfu.edu. Fig. 1. Detecting actions in videos. To improve the approximation, we use the following successive convexification scheme: Set initial trust region for each site the same size as target image Calculate matching costs for all possible candidate target points Find lower convex hull vertices in trust regions and target point basis sets Build and solve LP relaxation Trust region small? Update control points Update trust regions No Yes Output results Delaunay Triangulation of feature points on template images Fig. 5. Diagram of successive convexification. Comparison of SC-LP, ICM and BP with ground truth data: Fig. 6. Random sequence matching. Fig. 7. Random sequence matching with duplicated objects. Matching in clutter: Fig. 8. (c, d): Target image features and edge maps; (e, f): SC-LP matching result; (g, h): Greedy scheme result; (I, j): Chamfer matching result; (k, l): BP result. Fig. 10. Finding gestures in sign language sequence. Fig. 4 Searching process of LP. Fig. 11. Finding actions in an indoor sequence. Table 1. Detection Confusion Matrix Fig. 9. Finding actions in a standard dataset. (a, b, c, d): Templates and sample results. Fig. 12. Finding actions in a baseball sequence. Template Action Detection Introduction Method Experimental Results Conclusion