Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Matching Shape Sequences in Video with Applications in Human Movement Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
View Invariance for Human Action Recognition
International Journal of Computer Vision
Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Modeling and recognition of complex multi-person interactions in video
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Physics-based activity modelling in phase space
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Modeling multi-object activities in phase space
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Interactive event detection in crowd scenes
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Modeling multi-object interactions using "string of feature graphs"
Computer Vision and Image Understanding
Structured analysis of the ISI Atomic Pair Actions dataset using workflows
Pattern Recognition Letters
Hi-index | 0.00 |
Analysis of activities in low-resolution videos or far fields is a research challenge which has not received much attention. In this application scenario, it is often the case that the motion of the objects in the scene is the only low-level information available, other features like shape or color being unreliable. Also, typical videos consist of interactions of multiple objects which pose a major vision challenge. This paper proposes a method to classify activities of multiple interacting objects in low-resolution video by modeling them through a set of novel discriminative features which rely only on the object tracks. The noisy tracks of multiple objects are transformed into a feature space that encapsulates the individual characteristics of the tracks, as well as their interactions. Based on this feature vector, we propose an energy minimization approach to optimally divide the object tracks and their relative distances into meaningful partitions, called "strings of motion-words". Distances between activities can now be computed by comparing two strings. Complex activities can be broken up into strings and comparisons done separately for each object or for their interactions. We test the efficacy of our approach to search all the instances of a given query in multiple real-life video datasets.