Object tracking through multiple cameras is a popular research topic in security and surveillance systems especially when human objects are the target. However, occlusion is one of the challenging problems for the tracking process. This paper proposes a multiple-camera-based cooperative tracking method to overcome the occlusion problem.  The paper presents a new model for combining convolutiona...

Though convolutional neural networks have made significant improvements to the task of video tracking, they come at the cost of being extremely computationally expensive. In this work, we make the observation that different frames in a video can require different levels of network complexity in order to track with high accuracy. To exploit this, we propose a fully convolutional Siamese network ...

Convolutional and bidirectional recurrent neural networks have achieved considerable performance gains as acoustic models in automatic speech recognition in recent years. Latest architectures unify long short-term memory, gated recurrent unit and convolutional neural networks by stacking these different neural network types on each other, and providing short and long-term features to different ...

Current convolutional neural networks algorithms for video object tracking spend the same amount of computation for each object and video frame. However, it is harder to track an object in some frames than others, due to the varying amount of clutter, scene complexity, amount of motion, and object’s distinctiveness against its background. We propose a depth-adaptive convolutional Siamese networ...

We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series. The model is inspired by standard autoregressive (AR) models and gating mechanisms used in recurrent neural networks. It involves an AR-like weighting system, where the final predictor is obtained as a weighted sum of adjusted regressors...

Visual saliency models have recently begun to incorporate deep learning to achieve predictive capacity much greater than previous unsupervised methods. However, most existing models predict saliency using local mechanisms limited to the receptive field of the network. We propose a model that incorporates global scene semantic information in addition to local information gathered by a convolutio...

Human activity recognition requires both visual and temporal cues, making it challenging to integrate these important modalities. The usual schemes for integration are averaging and fixing the weights of both features for all samples. However, how much weight is needed for each sample and modality, is still an open question. A mixture of experts via a gating Convolutional Neural Network (CNN) i...

Objects may appear at arbitrary scales in perspective images of a scene, posing a challenge for recognition systems that process an image at a fixed resolution. We propose a depth-aware gating module that adaptively chooses the pooling field size in a convolutional network architecture according to the object scale (inversely proportional to the depth) so that small details can be preserved for...

We propose a convolutional neural network, named genCNN, for word sequence prediction. Different from previous work on neural networkbased language modeling and generation (e.g., RNN or LSTM), we choose not to greedily summarize the history of words as a fixed length vector. Instead, we use a convolutional neural network to predict the next word with the history of words of variable length. Als...