A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Sequence Analysis via Partial Differential Equations
Journal of Mathematical Imaging and Vision
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
How Close Are We to Understanding V1?
Neural Computation
Disambiguating Visual Motion by Form-Motion Interaction--a Computational Model
International Journal of Computer Vision
Learning a dictionary of shape-components in visual cortex: comparison with neurons, humans and machines
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Detecting and segmenting humans in crowded scenes
Proceedings of the 15th international conference on Multimedia
Local velocity-adapted motion events for spatio-temporal recognition
Computer Vision and Image Understanding
Segmentation and Tracking of Multiple Humans in Crowded Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Action Recognition Using a Bio-Inspired Feedforward Spiking Network
International Journal of Computer Vision
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
A survey on vision-based human action recognition
Image and Vision Computing
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
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Motion is a key feature for a wide class of computer vision approaches to recognize actions. In this article, we show how to define bio-inspired features for action recognition. To do so, we start from a well-established bio-inspired motion model of cortical areas V1 and MT. The primary visual cortex, designated as V1, is the first cortical area encountered in the visual stream processing and early responses of V1 cells consist in tiled sets of selective spatiotemporal filters. The second cortical area of interest in this article is area MT where MT cells pool incoming information from V1 according to the shape and characteristic of their receptive field. To go beyond the classical models and following the observations from Xiao et al. [61], we propose here to model different surround geometries for MT cells receptive fields. Then, we define the so-called bio-inspired features associated to an input video, based on the average activity of MT cells. Finally, we show how these features can be used in a standard classification method to perform action recognition. Results are given for the Weizmann and KTH databases. Interestingly, we show that the diversity of motion representation at the MT level (different surround geometries), is a major advantage for action recognition. On the Weizmann database, the inclusion of different MT surround geometries improved the recognition rate from 63.01+/-2.07% up to 99.26+/-1.66% in the best case. Similarly, on the KTH database, the recognition rate was significantly improved with the inclusion of MT different surround geometries (from 47.82+/-2.71% up to 92.44+/-0.01% in the best case). We also discussed the limitations of the current approach which are closely related to the input video duration. These promising results encourage us to further develop bio-inspired models incorporating other brain mechanisms and cortical areas in order to deal with more complex videos.