
Semantic segmentation is a challenging and computationally heavy task in computer vision. Recent works have shown promising results applied to real-time semantic segmentation tasks such as autonomous driving cars, scene recognition, and robot navigation systems. In order to maintain fast inference speed, most of the existing networks for real-time semantic segmentation make use of light decoders, or they simply do not use them at all. This strategy helps to maintain their fast inference speed; however, their accuracy performance is significantly lower in comparison to non-real-time semantic segmentation networks. In order to reduce the accuracy gap between real-time and non-real-time segmentation networks, we introduce two key modules aimed to improve the decoder of a high-performance baseline network for real-time semantic segmentation. Our first module, Dilated Asymmetric Pyramidal Fusion (DAPF), is designed to substantially increase the receptive field on the top of the last stage of the encoder, obtaining richer contextual features. Our second module, Multi-resolution Dilated Asymmetric (MDA) module, fuses multi-resolution feature maps coming from both, early and deeper stages of the network, simultaneously refining detail and contextual information in multiple stages of the decoder. Both modules exploit contextual information without excessively increasing the computational complexity of the network. Our proposed network entitled “FASSDNet” reaches 78.8% of mIoU accuracy on Cityscapes validation dataset at 41.1 FPS on full resolution images (1024x2048). In addition, with a lite version of our network, we reach 74.1% of mIoU at 133.1 FPS (full resolution) on a single NVIDIA GTX 1080Ti card with no additional acceleration techniques.

Fast and Accurate Real-Time Semantic Segmentation with Dilated Asymmetric Convolutions

Continuous hand gesture recognition (HGR) is an essential part of human-computer interaction with a wide range of applications in the automotive sector, consumer electronics, home automation, and others. In recent years, accurate and efficient deep learning models have been proposed for HGR. However, in the research community, the current publicly available datasets lack real-world elements needed to build responsive and efficient HGR systems. In this paper, we introduce a new benchmark dataset named IPN Hand with sufficient size, variety, and real-world elements able to train and evaluate deep neural networks. This dataset contains more than 4,000 gesture samples and 800,000 RGB frames from 50 distinct subjects. We design 13 different static and dynamic gestures focused on interaction with touchless screens. We especially consider the scenario when continuous gestures are performed without transition states, and when subjects perform natural movements with their hands as non-gesture actions. Gestures were collected from about 30 diverse scenes, with real-world variation in background and illumination. With our dataset, the performance of three 3D-CNN models is evaluated on the tasks of isolated and continuous real-time HGR. Furthermore, we analyze the possibility of increasing the recognition accuracy by adding multiple modalities derived from RGB frames, i.e., optical flow and semantic segmentation, while keeping the real-time performance of the 3D-CNN model. Our empirical study also provides a comparison with the publicly available nvGesture (NVIDIA) dataset. The experimental results show that the state-of-the-art ResNext-101 model decreases about 30% accuracy when using our real-world dataset, demonstrating that the IPN Hand dataset can be used as a benchmark, and may help the community to step forward in the continuous HGR.

IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition

PS T5.5: Image Processing, Analysis and Medical Imaging

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


Gibran Benitez-Garcia

2

SHORT BIO

Presentations

Fast and Accurate Real-Time Semantic Segmentation with Dilated Asymmetric Convolutions

IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition

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