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By Caprara A.

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N · for i = 1, . . , numclasses base =max posterior probability, for class c by h , of a negative example in the · T j,c i j i validation set – for i = 1, . . , numclasses · Tcada =max HN normalized score, for class ci , of a negative example in the validation set i ——————————————————————————————————————— — returns Bn :OnlineBoost(HN , x, label) -Set the example’s initial weight λx = 1. - For each base model hn ,in the boosted classifier 1. Set z by sampling Poisson(λx ). 2. Do z times : hn ← OnlineBase(hn , x, label) 3.

Objects, specially people undergo a change in shape while moving. In addition, their motion is not constant. Both people and vehicles can accelerate, de-accelerate or make a complete change in their direction of motion. Thus, tracking in realistic scenarios is a hard problem. We formulate the object tracking problem as region tracking, where regions are 2D projections of objects on the image plane. We assume that regions can enter and exit the view space. They can undergo a change in motion and they can also get occluded by the other regions.

Another problem is the occurrence of simultaneous exit and entry of objects at the same scene location. We will now discuss these problems in detail. 1 Occlusion Occlusion occurs when an object is not visible in an image because some other object/structure is blocking its view . Tracking objects under occlusion is difficult because accurate position and velocity of an occluded object cannot be determined. Different cases of occlusion are described in the following, • Inter-object occlusion occurs when one object blocks the view of other objects in the field of view of the camera.

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A 3/4-Approximation Algorithm for Multiple Subset Sum by Caprara A.


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