17,444 research outputs found
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Current multi-person localisation and tracking systems have an over reliance
on the use of appearance models for target re-identification and almost no
approaches employ a complete deep learning solution for both objectives. We
present a novel, complete deep learning framework for multi-person localisation
and tracking. In this context we first introduce a light weight sequential
Generative Adversarial Network architecture for person localisation, which
overcomes issues related to occlusions and noisy detections, typically found in
a multi person environment. In the proposed tracking framework we build upon
recent advances in pedestrian trajectory prediction approaches and propose a
novel data association scheme based on predicted trajectories. This removes the
need for computationally expensive person re-identification systems based on
appearance features and generates human like trajectories with minimal
fragmentation. The proposed method is evaluated on multiple public benchmarks
including both static and dynamic cameras and is capable of generating
outstanding performance, especially among other recently proposed deep neural
network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer
Vision (WACV), 201
Visual to Sound: Generating Natural Sound for Videos in the Wild
As two of the five traditional human senses (sight, hearing, taste, smell,
and touch), vision and sound are basic sources through which humans understand
the world. Often correlated during natural events, these two modalities combine
to jointly affect human perception. In this paper, we pose the task of
generating sound given visual input. Such capabilities could help enable
applications in virtual reality (generating sound for virtual scenes
automatically) or provide additional accessibility to images or videos for
people with visual impairments. As a first step in this direction, we apply
learning-based methods to generate raw waveform samples given input video
frames. We evaluate our models on a dataset of videos containing a variety of
sounds (such as ambient sounds and sounds from people/animals). Our experiments
show that the generated sounds are fairly realistic and have good temporal
synchronization with the visual inputs.Comment: Project page:
http://bvision11.cs.unc.edu/bigpen/yipin/visual2sound_webpage/visual2sound.htm
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