The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

A hybrid egocentric video summarization method to improve the healthcare for Alzheimer patients

Alzheimer patients face difficulty to remember the identity of persons and performing daily life activities. This paper presents a hybrid method to generate the egocentric video summary of important people, objects and medicines to facilitate the Alzheimer patients to recall their deserted memories. Lifelogging video data analysis is used to recall the human memory; however, the massive amount of lifelogging data makes it a challenging task to select the most relevant content to educate the Alzheimer’s patient. To address the challenges associated with massive lifelogging content, static video summarization approach is applied to select the key-frames that are more relevant in the context of recalling the deserted memories of the Alzheimer patients. This paper consists of three main modules that are face, object, and medicine recognition. Histogram of oriented gradient features are used to train the multi-class SVM for face recognition. SURF descriptors are employed to extract the features from the input video frames that are then used to find the corresponding points between the objects in the input video and the reference objects stored in the database. Morphological operators are applied followed by the optical character recognition to recognize and tag the medicines for Alzheimer patients. The performance of the proposed system is evaluated on 18 real-world homemade videos. Experimental results signify the effectiveness of the proposed system in terms of providing the most relevant content to enhance the memory of Alzheimer patients