Deep Learning for Object Detection in the Real World – M. Fiore

  Machine learning

One of our main areas of focus in KiwiSecurity is the correct detection and tracking of people. Deep learning strategies have provided significant improvements in this area, but still have several limitations, such as their relatively slow inference times. In real-world scenarios, the system needs to work reliably in very complex situations, such as crowded environments, where the objects are severely occluded. Also, in many situations, it is necessary to detect and track people simultaneously in multiple scenes, while having limited hardware. Creating a system that is accurate even in complex scenes, while still performing faster than real time and not having heavy memory requirements, is a very difficult task.

In this talk, I will give an overview of the state of art of object detection, focusing on deep learning strategies, and show how KiwiSecurity is using deep learning for the task of people detection. In particular, I will focus on how the real world constraints have impacted our choice in selecting deep learning frameworks and models, and how we have used the strategy of model pruning to improve the inference time of our application.

Michelangelo Fiore is Computer Vision Developer at KiwiSecurity since June 2017. Previously did PhD studies in Human-Robot Interaction at LAAS/CNRS in Toulouse.

Language: English

- Talk
- Discussion
- Networking (ImpactHub)

Machine Learning Meetups (MLMU) is an independent platform for people interested in Machine Learning, Information Retrieval, Natural Language Processing, Computer Vision, Pattern Recognition, Data Journalism, Artificial Intelligence, Agent Systems and all the related topics. MLMU is a regular community meeting usually consisting of a talk, a discussion and a subsequent networking. At the end of the year 2016, MLMU spread also to Brno and Bratislava.