Multiple Object Tracking using K-Shortest Paths Optimization

Multi-object tracking can be achieved by detecting objects in individual frames and then linking detections across frames. Such an approach can be made very robust to the occasional detection failure: If an object is not detected in a frame but is in previous and following ones, a correct trajectory will nevertheless be produced. By contrast, a false-positive detection in a few frames will be ignored. However, when dealing with a multiple target problem, the linking step results in a difficult optimization problem in the space of all possible families of trajectories. This is usually dealt with by sampling or greedy search based on variants of Dynamic Programming which can easily miss the global optimum. In this paper, we show that reformulating that step as a constrained flow optimization results in a convex problem. We take advantage of its particular structure to solve it using the k-shortest paths algorithm, which is very fast. This new approach is far simpler formally and algorithmically than existing techniques and lets us demonstrate excellent performance in two very different contexts

TEMPORALLY CONSISTENT LAYER DEPTH ORDERING VIA PIXEL VOTING FOR PSEUDO 3D REPRESENTATION

A new region-based depth ordering algorithm is proposed based on the segmented motion layers with affine motion models. Starting from an initial set of layers that are independently extracted for each frame of an input sequence, relative depth order of every layer is determined following a bottom-to-top approach from local pair-wise relations to a global ordering. Layer sets of consecutive time instants are warped in two opposite directions in time to capture pair-wise occlusion relations of neighboring layers in the form of pixel voting statistics. Global depth order of layers is estimated by mapping the pair-wise relations to a directed acyclic graph and solving the longest path problem via a breadth-first search strategy. Temporal continuity is enforced both at the region segmentation and depth ordering stages to achieve temporally coherent layer support maps and depth order relations. Experimental results show that the proposed algorithm yields quite promising results even on dynamic scenes with multiple motions

Systems and methods for tracking interacting objects

Systems and methods for tracking interacting objects may acquire, with a sensor, and two or more images associated with two or more time instances. A processor may generate image data from the two or more images. The processor may apply an extended Probability Occupancy Map (POM) algorithm to the image data to obtain probability of occupancy for a container class of potentially interacting objects, probability of occupancy for a containee class of the potentially interacting objects, and a size relationship of the potentially interacting objects, over a set of discrete locations on a ground plane for each time instance. The processor may estimate trajectories of an object belonging to each of the two classes by determining a solution of a tracking model on the basis of the occupancy probabilities and a set of rules describing the interaction between objects of different or the same classes