A tracklet is a short sequence of detections of an entity of interest, such as a person's face, in contiguous frames of a video. In this paper we consider clustering tracklets in long videos, an important problem of current interest in Computer Vision. It involves identifying tracklets in short segments of the video and finding associations between tracklets over the entire video. These tasks require careful modelling of spatio-temporal cues. State of the art [27] proposes a probabilistic model which incorporates the spatio-temporal cues by parametrizing them through clique potentials in HMRF. The attendant learning and inference problems make the model un-wieldy resulting in failure to handle long videos with many cluster labels. In this paper we circumvent the problem of explicitly encoding spatio-temporal cues by exploiting Temporal Coherence (TC). The major contribution of the paper is to develop Temporally Coherent Chinese Restaurant Process (TC-CRP), a novel Bayesian Non-parametric (BNP) prior which models Temporal Coherence. To the best of our knowledge this is the first work which models TC in a BNP framework and thus makes a significant addition to BNP priors suited for video analytics. On an interesting problem of discovering persons and their tracklets from user-uploaded videos of long TV series episodes from Youtube we show that TC-CRP is very effective. It can also filter out tracklets resulting from false detections. We explore alternative approaches based on low-rank matrix recovery and constrained subspace clustering, but find these to be very slow and less accurate than our method. Finally, we show that TC-CRP can be useful in Low-rank Matrix Recovery when the desired matrix has sets of identical columns.
is a core topic in computer vision, in which a target object is located in each frame based on appearance similarity and spatio- temporal locality. A more advanced task is @cite_27 , in which several targets are present per frame. A tracking paradigm that is particularly helpful in multi-target tracking is @cite_16 , where object-specific detectors like @cite_9 are run per frame (or on a subset of frames), and the detection responses are linked to form tracks. From this came the concept of @cite_11 which attempts to do the linking hierarchically. This requires pairwise similarity measures between tracklets. Multi-target tracking via tracklets is usually cast as Bipartite Matching, which is solved using Hungarian Algorithm. Tracklet association and face clustering are done simultaneously in @cite_20 using HMRF. The main difference of face tracklet clustering and person discovery is that, the number of clusters to be formed is not known in the latter.
@cite_9 @cite_27 @cite_16 @cite_20 @cite_11
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We address the problem of multi-person tracking in a complex scene from a single camera. Although tracklet-association methods have shown impressive results in several challenging datasets, discriminability of the appearance model remains a limitation. Inspired by the work of person identity recognition, we obtain discriminative appearance-based affinity models by a novel framework to incorporate the merits of person identity recognition, which help multi-person tracking performance. During off-line learning, a small set of local image descriptors is selected to be used in on-line learned appearances-based affinity models effectively and efficiently. Given short but reliable track-lets generated by frame-to-frame association of detection responses, we identify them as query tracklets and gallery tracklets. For each gallery tracklet, a target-specific appearance model is learned from the on-line training samples collected by spatio-temporal constraints. Both gallery tracklets and query tracklets are fed into hierarchical association framework to obtain final tracking results. We evaluate our proposed system on several public datasets and show significant improvements in terms of tracking evaluation metrics. This paper addresses the problem of simultaneous tracking of multiple targets in a video. We first apply object detectors to every video frame. Pairs of detection responses from every two consecutive frames are then used to build a graph of tracklets. The graph helps transitively link the best matching tracklets that do not violate hard and soft contextual constraints between the resulting tracks. We prove that this data association problem can be formulated as finding the maximum-weight independent set (MWIS) of the graph. We present a new, polynomial-time MWIS algorithm, and prove that it converges to an optimum. Similarity and contextual constraints between object detections, used for data association, are learned online from object appearance and motion properties. Long-term occlusions are addressed by iteratively repeating MWIS to hierarchically merge smaller tracks into longer ones. Our results demonstrate advantages of simultaneously accounting for soft and hard contextual constraints in multitarget tracking. We outperform the state of the art on the benchmark datasets. We propose an approach for multi-pose face tracking by association of face detection responses in two stages using multiple cues. The low-level stage uses a two-threshold strategy to merge detection responses based on location, size and pose, resulting in short but reliable tracklets. The high-level stage uses different cues for computing a joint similarity measure between tracklets. The facial cue compares facial features of the most frontal face detections in pairs of tracklets. The classifier cue learns a discriminative appearance model for each tracklet, using detection pairs within reliable tracklets and between overlapping tracklets as training data. The constraint cue observes the compatibility of motion of two tracklets. The association of tracklets is globally optimized with the Hungarian algorithm. We validate our approach on two challenging episodes of two TV series and report a Multiple Object Tracking Accuracy (MOTA) of 82 and 68.2 , respectively. Tracking multiple targets in a video, based on a finite set of detection hypotheses, is a persistent problem in computer vision. A common strategy for tracking is to first select hypotheses spatially and then to link these over time while maintaining disjoint path constraints [14, 15, 24]. In crowded scenes multiple hypotheses will often be similar to each other making selection of optimal links an unnecessary hard optimization problem due to the sequential treatment of space and time. Embracing this observation, we propose to link and cluster plausible detections jointly across space and time. Specifically, we state multi-target tracking as a Minimum Cost Subgraph Multicut Problem. Evidence about pairs of detection hypotheses is incorporated whether the detections are in the same frame, neighboring frames or distant frames. This facilitates long-range re-identification and within-frame clustering. Results for published benchmark sequences demonstrate the superiority of this approach.