2,995 research outputs found
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation
Joint segmentation and classification of fine-grained actions is important
for applications of human-robot interaction, video surveillance, and human
skill evaluation. However, despite substantial recent progress in large-scale
action classification, the performance of state-of-the-art fine-grained action
recognition approaches remains low. We propose a model for action segmentation
which combines low-level spatiotemporal features with a high-level segmental
classifier. Our spatiotemporal CNN is comprised of a spatial component that
uses convolutional filters to capture information about objects and their
relationships, and a temporal component that uses large 1D convolutional
filters to capture information about how object relationships change across
time. These features are used in tandem with a semi-Markov model that models
transitions from one action to another. We introduce an efficient constrained
segmental inference algorithm for this model that is orders of magnitude faster
than the current approach. We highlight the effectiveness of our Segmental
Spatiotemporal CNN on cooking and surgical action datasets for which we observe
substantially improved performance relative to recent baseline methods.Comment: Updated from the ECCV 2016 version. We fixed an important
mathematical error and made the section on segmental inference cleare
Network Analysis with Stochastic Grammars
Digital forensics requires significant manual effort to identify items of evidentiary interest from the ever-increasing volume of data in modern computing systems. One of the tasks digital forensic examiners conduct is mentally extracting and constructing insights from unstructured sequences of events. This research assists examiners with the association and individualization analysis processes that make up this task with the development of a Stochastic Context -Free Grammars (SCFG) knowledge representation for digital forensics analysis of computer network traffic. SCFG is leveraged to provide context to the low-level data collected as evidence and to build behavior profiles. Upon discovering patterns, the analyst can begin the association or individualization process to answer criminal investigative questions. Three contributions resulted from this research. First , domain characteristics suitable for SCFG representation were identified and a step -by- step approach to adapt SCFG to novel domains was developed. Second, a novel iterative graph-based method of identifying similarities in context-free grammars was developed to compare behavior patterns represented as grammars. Finally, the SCFG capabilities were demonstrated in performing association and individualization in reducing the suspect pool and reducing the volume of evidence to examine in a computer network traffic analysis use case
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