Where Am I? Comparing CNN and LSTM for Location Classification in Egocentric Videos

Egocentric vision is a technology that exists in a variety of fields such as life-logging, sports recording and robot navigation. Plenty of research work focuses on location detection and activity recognition, with applications in the area of Ambient Assisted Living. The basis of this work is the idea that locations can be characterized by the presence of specific objects. Our objective is the recognition of locations in egocentric videos that mainly consist of indoor house scenes. We perform an extensive comparison between Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based classification methods that aim at finding the in-house location by classifying the detected objects which are extracted with a state-of-the-art object detector. We show that location classification is affected by the quality of the detected objects, i.e. the false detections among the correct ones in a series of frames, but this effect can be greatly limited by taking into account the temporal structure of the information by using LSTM. Finally, we argue about the potential for useful real-world applications

Where Am I? Comparing CNN and LSTM for Location Classification in Egocentric Videos

Egocentric vision is a technology that exists in a variety of fields such as life-logging, sports recording and robot navigation. Plenty of research work focuses on location detection and activity recognition, with applications in the area of Ambient Assisted Living. The basis of this work is the idea that locations can be characterized by the presence of specific objects. Our objective is the recognition of locations in egocentric videos that mainly consist of indoor house scenes. We perform an extensive comparison between Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based classification methods that aim at finding the in-house location by classifying the detected objects which are extracted with a state-of-the-art object detector. We show that location classification is affected by the quality of the detected objects, i.e. the false detections among the correct ones in a series of frames, but this effect can be greatly limited by taking into account the temporal structure of the information by using LSTM. Finally, we argue about the potential for useful real-world applications

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions