62 research outputs found
Human Detection and Tracking for Video Surveillance A Cognitive Science Approach
With crimes on the rise all around the world, video surveillance is becoming
more important day by day. Due to the lack of human resources to monitor this
increasing number of cameras manually new computer vision algorithms to perform
lower and higher level tasks are being developed. We have developed a new
method incorporating the most acclaimed Histograms of Oriented Gradients the
theory of Visual Saliency and the saliency prediction model Deep Multi Level
Network to detect human beings in video sequences. Furthermore we implemented
the k Means algorithm to cluster the HOG feature vectors of the positively
detected windows and determined the path followed by a person in the video. We
achieved a detection precision of 83.11% and a recall of 41.27%. We obtained
these results 76.866 times faster than classification on normal images.Comment: ICCV 2017 Venice, Italy Pages 5 Figures
WAYLA - Generating Images from Eye Movements
We present a method for reconstructing images viewed by observers based only
on their eye movements. By exploring the relationships between gaze patterns
and image stimuli, the "What Are You Looking At?" (WAYLA) system learns to
synthesize photo-realistic images that are similar to the original pictures
being viewed. The WAYLA approach is based on the Conditional Generative
Adversarial Network (Conditional GAN) image-to-image translation technique of
Isola et al. We consider two specific applications - the first, of
reconstructing newspaper images from gaze heat maps, and the second, of
detailed reconstruction of images containing only text. The newspaper image
reconstruction process is divided into two image-to-image translation
operations, the first mapping gaze heat maps into image segmentations, and the
second mapping the generated segmentation into a newspaper image. We validate
the performance of our approach using various evaluation metrics, along with
human visual inspection. All results confirm the ability of our network to
perform image generation tasks using eye tracking data
Saliency Prediction for Mobile User Interfaces
We introduce models for saliency prediction for mobile user interfaces. A
mobile interface may include elements like buttons, text, etc. in addition to
natural images which enable performing a variety of tasks. Saliency in natural
images is a well studied area. However, given the difference in what
constitutes a mobile interface, and the usage context of these devices, we
postulate that saliency prediction for mobile interface images requires a fresh
approach. Mobile interface design involves operating on elements, the building
blocks of the interface. We first collected eye-gaze data from mobile devices
for free viewing task. Using this data, we develop a novel autoencoder based
multi-scale deep learning model that provides saliency prediction at the mobile
interface element level. Compared to saliency prediction approaches developed
for natural images, we show that our approach performs significantly better on
a range of established metrics.Comment: Paper accepted at WACV 201
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