Visual object tracking based on the object's salient features with application in automatic nutrition assistance

Olga Zoidi, Anastasios Tefas, Ioannis Pitas

Abstract: A novel method for object tracking in videos which can find application in eating and drinking activity recognition is proposed. The query object is detected in the first video frame, extracting a new query image. The initial query image along with the obtained query image are then compared with patches within a determined search region around the position of the detected object in the previous frame. For each image, the local steering kernels are extracted and the similarity between a query image and the patches of the video frame is measured by calculating the cosine similarity. The proposed method finds application in eating and drinking activity recognition.