A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns called motifs from documents given as word $$\times$$ time count matrices (e.g., videos). In this model, documents are represented as a mixture of sequential activity patterns (our motifs) where the mixing weights are defined by the motif starting time occurrences. The novelties are multi fold. First, unlike previous approaches where topics modeled only the co-occurrence of words at a given time instant, our motifs model the co-occurrence and temporal order in which the words occur within a temporal window. Second, unlike traditional Dynamic Bayesian networks (DBN), our model accounts for the important case where activities occur concurrently in the video (but not necessarily in synchrony), i.e., the advent of activity motifs can overlap. The learning of the motifs in these difficult situations is made possible thanks to the introduction of latent variables representing the activity starting times, enabling us to implicitly align the occurrences of the same pattern during the joint inference of the motifs and their starting times. As a third novelty, we propose a general method that favors the recovery of sparse distributions, a highly desirable property in many topic model applications, by adding simple regularization constraints on the searched distributions to the data likelihood optimization criteria. We substantiate our claims with experiments on synthetic data to demonstrate the algorithm behavior, and on four video datasets with significant variations in their activity content obtained from static cameras. We observe that using low-level motion features from videos, our algorithm is able to capture sequential patterns that implicitly represent typical trajectories of scene object

A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns called motifs from documents given as word×time count matrices (e.g., videos). In this model, documents are represented as a mixture of sequential activity patterns (our motifs) where the mixing weights are defined by the motif starting time occurrences. The novelties are multi fold. First, unlike previous approaches where topics modeled only the co-occurrence of words at a given time instant, our motifs model the co-occurrence and temporal order in which the words occur within a temporal window. Second, unlike traditional Dynamic Bayesian Networks (DBN), our model accounts for the important case where activities occur concurrently in the video (but not necessarily in syn- chrony), i.e., the advent of activity motifs can overlap. The learning of the motifs in these difficult situations is made possible thanks to the introduction of latent variables representing the activity starting times, enabling us to implicitly align the occurrences of the same pattern during the joint inference of the motifs and their starting times. As a third novelty, we propose a general method that favors the recovery of sparse distributions, a highly desirable property in many topic model applications, by adding simple regularization constraints on the searched distributions to the data likelihood optimization criteria. We substantiate our claims with experiments on synthetic data to demonstrate the algorithm behavior, and on four video datasets with significant variations in their activity content obtained from static cameras. We observe that using low-level motion features from videos, our algorithm is able to capture sequential patterns that implicitly represent typical trajectories of scene objects

LOCALIZED TEMPORAL PROFILE OF SURVEILLANCE VIDEO

Surveillance videos are recorded pervasively and their retrieval currently still relies on human operators. As an intermediate representation, this work develops a new temporal profile of video to convey accurate temporal information in the video while keeping certain spatial characteristics of targets of interest for recognition. The profile is obtained at critical positions where major target flow appears. We set a sampling line crossing the motion direction to profile passing targets in the temporal domain. In order to add spatial information to the temporal profile to certain extent, we integrate multiple profiles from a set of lines with blending method to reflect the target motion direction and position in the temporal profile. Different from mosaicing/montage methods for video synopsis in spatial domain, our temporal profile has no limit on the time length, and the created profile significantly reduces the data size for brief indexing and fast search of video

Temporal Mapping of Surveillance Video for Indexing and Summarization

This work converts the surveillance video to a temporal domain image called temporal profile that is scrollable and scalable for quick searching of long surveillance video by human operators. Such a profile is sampled with linear pixel lines located at critical locations in the video frames. It has precise time stamp on the target passing events through those locations in the field of view, shows target shapes for identification, and facilitates the target search in long videos. In this paper, we first study the projection and shape properties of dynamic scenes in the temporal profile so as to set sampling lines. Then, we design methods to capture target motion and preserve target shapes for target recognition in the temporal profile. It also provides the uniformed resolution of large crowds passing through so that it is powerful in target counting and flow measuring. We also align multiple sampling lines to visualize the spatial information missed in a single line temporal profile. Finally, we achieve real time adaptive background removal and robust target extraction to ensure long-term surveillance. Compared to the original video or the shortened video, this temporal profile reduced data by one dimension while keeping the majority of information for further video investigation. As an intermediate indexing image, the profile image can be transmitted via network much faster than video for online video searching task by multiple operators. Because the temporal profile can abstract passing targets with efficient computation, an even more compact digest of the surveillance video can be created

Discovering activity patterns in office environment using a network of low-resolution visual sensors

Understanding activity patterns in office environments is important in order to increase workers’ comfort and productivity. This paper proposes an automated system for discovering activity patterns of multiple persons in a work environment using a network of cheap low-resolution visual sensors (900 pixels). Firstly, the users’ locations are obtained from a robust people tracker based on recursive maximum likelihood principles. Secondly, based on the users’ mobility tracks, the high density positions are found using a bivariate kernel density estimation. Then, the hotspots are detected using a confidence region estimation. Thirdly, we analyze the individual’s tracks to find the starting and ending hotspots. The starting and ending hotspots form an observation sequence, where the user’s presence and absence are detected using three powerful Probabilistic Graphical Models (PGMs). We describe two approaches to identify the user’s status: a single model approach and a two-model mining approach. We evaluate both approaches on video sequences captured in a real work environment, where the persons’ daily routines are recorded over 5 months. We show how the second approach achieves a better performance than the first approach. Routines dominating the entire group’s activities are identified with a methodology based on the Latent Dirichlet Allocation topic model. We also detect routines which are characteristic of persons. More specifically, we perform various analysis to determine regions with high variations, which may correspond to specific events