Alignment of many 3D point clouds, possibly captured by multiple devices at different times, is a critical step for increasingly popular applications such as 3D model construction and augmented reality. For very large data sets, traditional methods such as ICP can become computationally intractable, or produce poor results. We present an efficient method for accurately aligning very large numbers of dense 3D point clouds, and apply it to a city-scale data set. The method relies on the novel combination of 1) partitioning the point clouds based on loop structures detected across a combined network of all device capture paths, and 2) making use of the loop closure property to accurately align point clouds within each sub-problem. Final global alignment of the loop-based results is formulated as a least squares optimization with closed form solution. Experimental results are shown for aligning 3D points across the entire city of San Francisco with centimeter-scale accuracy, via an efficient parallelized architecture.

In this paper, we show that tracking multiple people whose paths may intersect can be formulated as a multi-commodity network flow problem. Our proposed framework is designed to exploit image appearance cues to prevent identity switches. Our method is effective even when such cues are only available at distant time intervals. This is unlike many current approaches that depend on appearance being exploitable from frame-to-frame. Furthermore, our algorithm lends itself to a real-time implementation. We validate our approach on three publicly available datasets that contain long and complex sequences, the APIDIS basketball dataset, the ISSIA soccer dataset, and the PETS'09 pedestrian dataset. We also demonstrate its performance on a newer basketball dataset that features complete world championship basketball matches. In all cases, our approach preserves identity better than state-of-the-art tracking algorithms.

In this paper, we show that tracking multiple people whose paths may intersect can be formulated as a convex global optimization problem. Our proposed framework is designed to exploit image appearance cues to prevent identity switches. Our method is effective even when such cues are only available at distant time intervals. This is unlike many current approaches that depend on appearance being exploitable from frame to frame. We validate our approach on three multi-camera sport and pedestrian datasets that contain long and complex sequences. Our algorithm perseveres identities better than state-of-the-art algorithms while keeping similar MOTA scores.

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.

Given two to four synchronized video streams taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes. In addition, we also derive metrically accurate trajectories for each one of them. Our contribution is twofold. First, we demonstrate that our generative model can effectively handle occlusions in each time frame independently, even when the only data available comes from the output of a simple background subtraction algorithm and when the number of individuals is unknown a priori. Second, we show that multi-person tracking can be reliably achieved by processing individual trajectories separately over long sequences, provided that a reasonable heuristic is used to rank these individuals and avoid confusing them with one another.

We present a multi-view change detection approach aimed at being robust \nwith respect to common “disturbance factors” yielding image changes in realworld \napplications. Disturbance factors causing “slow” or “fast-and-global” \nimage variations, such as light changes and dynamic adjustments of camera \nparameters (e.g. auto-exposure and auto-gain control), are dealt with by a \nproper single-view change detector run independently on each view. The \ncomputed change masks are then fused into a “synergy mask” defined into a \ncommon virtual top-view, so as to detect and filter-out “fast-and-local” image \nchanges due to physical points lying on the ground surface (e.g. shadows cast \nby moving objects and light spots hitting the ground surface).

Given three or four synchronized videos taken at eye level and from different angles, we show that we can effectively use dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions. In addition, we also derive metrically accurate trajectories for each one of them. Our main contribution is to show that multi-person tracking can be reliably achieved by processing individual trajectories separately over long sequences, provided that a reasonable heuristic is used to rank these individuals and avoid confusing them with one another. In this way, we achieve robustness by finding optimal trajectories over many frames while avoiding the combinatorial explosion that would result from simultaneously dealing with all the individuals.

This paper presents a noise-robust double-talk detection algorithm based on normalized cross-correlation and a noise offset. The noise offset alleviates undesirable influence by the background noise existing in the microphone signal. It is estimated from the echo-cancelled signal when its autocorrelation is low and its power is smaller than the echo-replica power. A detection threshold of the new normalized cross-correlation is adaptively controlled based on the echo-to-NES (near-end speech) ratio. Simulation results demonstrate superior performance of the new algorithm.