SiaMemory: Target Tracking

This paper proposes, develops and evaluates a novel object-tracking algorithm that outperforms start-of-the-art method in terms of its robustness. The proposed method compromises Siamese networks, Recurrent Convolutional Neural Networks (RCNNs) and Long Short Term Memory (LSTM) and performs short-term target tracking in real-time. As Siamese networks only generates the current frame tracking target based on the previous frame of image information, it is less effective in handling target’s appearance and disappearance, rapid movement, or deformation. Hence, our method a novel tracking method that integrates improved full-convolutional Siamese networks based on all-CNN, RCNN and LSTM. In order to improve the training efficiency of the deep learning network, a strategy of segmented training based on transfer learning is proposed. For some test video sequences that background clutters, deformation, motion blur, fast motion and out of view, our method achieves the best tracking performance. Using 41 videos from the Object Tracking Benchmark (OTB) dataset and considering the area under the curve for the precision and success rate, our method outperforms the second best by 18.5% and 14.9% respectively

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

Online Visual Robot Tracking and Identification using Deep LSTM Networks

Collaborative robots working on a common task are necessary for many applications. One of the challenges for achieving collaboration in a team of robots is mutual tracking and identification. We present a novel pipeline for online visionbased detection, tracking and identification of robots with a known and identical appearance. Our method runs in realtime on the limited hardware of the observer robot. Unlike previous works addressing robot tracking and identification, we use a data-driven approach based on recurrent neural networks to learn relations between sequential inputs and outputs. We formulate the data association problem as multiple classification problems. A deep LSTM network was trained on a simulated dataset and fine-tuned on small set of real data. Experiments on two challenging datasets, one synthetic and one real, which include long-term occlusions, show promising results.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, 2017. IROS RoboCup Best Paper Awar