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.
Finally, has been studied for a few years in the Computer Vision community. The aim is to provide a short but comprehensive summary of videos. This summary is usually in the form of a few , and sometimes as a short segment of the video around these keyframes. A recent example is @cite_2 which models a video as a matrix, each frame as a column, and each keyframe as a , in terms of which the other columns are expressed. A more recent work @cite_34 considers a kernel matrix to encode similarities between pairs of frames, uses it for of the video, assigns an importance label to each of these segments using an SVM (trained from segmented and labelled videos), and creates the summary with the important segments. However, such summaries are in terms of low-level visual features, rather than high-level semantic features which humans use. An attempt to bridge this gap was made in @cite_35 , which defined movie scenes and summaries in terms of characters. This work used face detections along with for semantic segmentation into shots and scenes, which were used for summarization.
@cite_35 @cite_34 @cite_2
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A decent movie summary is helpful for movie producer to promote the movie as well as audience to capture the theme of the movie before watching the whole movie. Most exiting automatic movie summarization approaches heavily rely on video content only, which may not deliver ideal result due to the semantic gap between computer calculated low-level features and human used high-level understanding. In this paper, we incorporate script into movie analysis and propose a novel character-based movie summarization approach, which is validated by modern film theory that what actually catches audiences' attention is the character. We first segment scenes in the movie by analysis and alignment of script and movie. Then we conduct substory discovery and content attention analysis based on the scent analysis and character interaction features. Given obtained movie structure and content attention value, we calculate movie attraction scores at both shot and scene levels and adopt this as criterion to generate movie summary. The promising experimental results demonstrate that character analysis is effective for movie summarization and movie content understanding. The rapid growth of consumer videos requires an effective and efficient content summarization method to provide a user-friendly way to manage and browse the huge amount of video data. Compared with most previous methods that focus on sports and news videos, the summarization of personal videos is more challenging because of its unconstrained content and the lack of any pre-imposed video structures. We formulate video summarization as a novel dictionary selection problem using sparsity consistency, where a dictionary of key frames is selected such that the original video can be best reconstructed from this representative dictionary. An efficient global optimization algorithm is introduced to solve the dictionary selection model with the convergence rates as O(1 K2) (where K is the iteration counter), in contrast to traditional sub-gradient descent methods of O(1 √K). Our method provides a scalable solution for both key frame extraction and video skim generation, because one can select an arbitrary number of key frames to represent the original videos. Experiments on a human labeled benchmark dataset and comparisons to the state-of-the-art methods demonstrate the advantages of our algorithm.