Semi-Supervised First-Person Activity Recognition in Body-Worn Video

Body-worn cameras are now commonly used for logging daily life, sports, and law enforcement activities, creating a large volume of archived footage. This paper studies the problem of classifying frames of footage according to the activity of the camera-wearer with an emphasis on application to real-world police body-worn video. Real-world datasets pose a different set of challenges from existing egocentric vision datasets: the amount of footage of different activities is unbalanced, the data contains personally identifiable information, and in practice it is difficult to provide substantial training footage for a supervised approach. We address these challenges by extracting features based exclusively on motion information then segmenting the video footage using a semi-supervised classification algorithm. On publicly available datasets, our method achieves results comparable to, if not better than, supervised and/or deep learning methods using a fraction of the training data. It also shows promising results on real-world police body-worn video

Fly eyes are not still: a motion illusion in Drosophila flight supports parallel visual processing.

Most animals shift gaze by a 'fixate and saccade' strategy, where the fixation phase stabilizes background motion. A logical prerequisite for robust detection and tracking of moving foreground objects, therefore, is to suppress the perception of background motion. In a virtual reality magnetic tether system enabling free yaw movement, Drosophila implemented a fixate and saccade strategy in the presence of a static panorama. When the spatial wavelength of a vertical grating was below the Nyquist wavelength of the compound eyes, flies drifted continuously and gaze could not be maintained at a single location. Because the drift occurs from a motionless stimulus - thus any perceived motion stimuli are generated by the fly itself - it is illusory, driven by perceptual aliasing. Notably, the drift speed was significantly faster than under a uniform panorama, suggesting perceptual enhancement as a result of aliasing. Under the same visual conditions in a rigid-tether paradigm, wing steering responses to the unresolvable static panorama were not distinguishable from those to a resolvable static pattern, suggesting visual aliasing is induced by ego motion. We hypothesized that obstructing the control of gaze fixation also disrupts detection and tracking of objects. Using the illusory motion stimulus, we show that magnetically tethered Drosophila track objects robustly in flight even when gaze is not fixated as flies continuously drift. Taken together, our study provides further support for parallel visual motion processing and reveals the critical influence of body motion on visuomotor processing. Motion illusions can reveal important shared principles of information processing across taxa

Boosted Multiple Kernel Learning for First-Person Activity Recognition

Activity recognition from first-person (ego-centric) videos has recently gained attention due to the increasing ubiquity of the wearable cameras. There has been a surge of efforts adapting existing feature descriptors and designing new descriptors for the first-person videos. An effective activity recognition system requires selection and use of complementary features and appropriate kernels for each feature. In this study, we propose a data-driven framework for first-person activity recognition which effectively selects and combines features and their respective kernels during the training. Our experimental results show that use of Multiple Kernel Learning (MKL) and Boosted MKL in first-person activity recognition problem exhibits improved results in comparison to the state-of-the-art. In addition, these techniques enable the expansion of the framework with new features in an efficient and convenient way.Comment: First published in the Proceedings of the 25th European Signal Processing Conference (EUSIPCO-2017) in 2017, published by EURASI

Robust automatic target tracking based on a Bayesian ego-motion compensation framework for airborne FLIR imagery

Automatic target tracking in airborne FLIR imagery is currently a challenge due to the camera ego-motion. This phenomenon distorts the spatio-temporal correlation of the video sequence, which dramatically reduces the tracking performance. Several works address this problem using ego-motion compensation strategies. They use a deterministic approach to compensate the camera motion assuming a specific model of geometric transformation. However, in real sequences a specific geometric transformation can not accurately describe the camera ego-motion for the whole sequence, and as consequence of this, the performance of the tracking stage can significantly decrease, even completely fail. The optimum transformation for each pair of consecutive frames depends on the relative depth of the elements that compose the scene, and their degree of texturization. In this work, a novel Particle Filter framework is proposed to efficiently manage several hypothesis of geometric transformations: Euclidean, affine, and projective. Each type of transformation is used to compute candidate locations of the object in the current frame. Then, each candidate is evaluated by the measurement model of the Particle Filter using the appearance information. This approach is able to adapt to different camera ego-motion conditions, and thus to satisfactorily perform the tracking. The proposed strategy has been tested on the AMCOM FLIR dataset, showing a high efficiency in the tracking of different types of targets in real working conditions