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We present an approach to reconstruct the 3D shape of multiple deforming objects from incomplete 2D trajectories acquired by a single camera. Additionally, we simultaneously provide spatial segmentation (i.e., we identify each of the objects in every frame) and temporal clustering (i.e., we split the sequence into primitive actions). This advances existing work, which only tackled the problem for...
In this paper, we present the first multi-body non-rigid structure-from-motion (SFM) method, which simultaneously reconstructs and segments multiple objects that are undergoing non-rigid deformation over time. Under our formulation, 3D trajectories for each non-rigid object can be well approximated with a sparse affine combination of other 3D trajectories from the same object. The resultant optimization...
This work proposes a trajectory clustering-based approach for segmenting flow patterns in high density crowd videos. The goal is to produce a pixel-wise segmentation of a video sequence (static camera), where each segment corresponds to a different motion pattern. Unlike previous studies that use only motion vectors, we extract full trajectories so as to capture the complete temporal evolution of...
Many scenes that we would like to reconstruct contain articulated objects, and are often captured by only a single, non-fixed camera. Existing techniques for reconstructing articulated objects either require templates, which can be challenging to acquire, or have difficulties with perspective effects and missing data. In this paper, we present a novel reconstruction pipeline that first treats each...
The objective of this paper is to parse object trajectories in surveillance video against occlusion, interruption, and background clutter. We present a spatio-temporal graph (ST-Graph) representation and a cluster sampling algorithm via deferred inference. An object trajectory in the ST-Graph is represented by a bundle of ldquomotion primitivesrdquo, each of which consists of a small number of matched...
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