Multi-Template Temporal Siamese Network for Long-Term Object Tracking

Siamese Networks are one of most popular visual object tracking methods for their high speed and high accuracy tracking ability as long as the target is well identified. However, most Siamese Network based trackers use the first frame as the ground truth of an object and fail when target appearance changes significantly in next frames. They also have dif iculty distinguishing the target from similar other objects in the frame. We propose two ideas to solve both problems. The first idea is using a bag of dynamic templates, containing diverse, similar, and recent target features and continuously updating it with diverse target appearances. The other idea is to let a network learn the path history and project a potential future target location in a next frame. This tracker achieves state-of-the-art performance on the long-term tracking dataset UAV20L by improving the success rate by a large margin of 15% (65.4 vs 56.6) compared to the state-of-the-art method, HiFT. The of icial python code of this paper is publicly available

Music emotion identification from lyrics

ABSTRACT-Very large online music databases have recently been created by vendors, but they generally lack content-based retrieval methods. One exception is Allmusic.com which offers browsing by musical emotion, using human experts to classify several thousand songs into 183 moods. In this paper, machine learning techniques are used instead of human experts to extract emotions in Music. The classification is based on a psychological model of emotion that is extended to 23 specific emotion categories. Our results for mining the lyrical text of songs for specific emotion are promising, generate classification models that are human-comprehensible, and generate results that correspond to commonsense intuitions about specific emotions. Mining lyrics focused in this paper is one aspect of research which combines different classifiers of musical emotion such as acoustics and lyrical text