Omron Corporation

Beijing Sensetime Technology Development Co., Ltd.

Toyota Motor Engineering & Manufacturing North America, Inc.

Hong Kong Applied Science and Technology Research Institute Company, Limited

Huchuan Lu

Xiang Ruan

Xiaogang Wang

Wanli Ouyang

Lijun Wang

Danil V Prokhorov

Xue Mei

Lu Wang

Shangping Liu

Pingping Zhang

Na Tong

Luning Liu

Lihe Zhang

Xiaohui Li

Disclosed are methods, apparatuses and systems for CNN network adaption and object online tracking. The CNN network adaption method comprises: transforming a first feature map into a plurality of sub-feature maps, wherein the first feature map is generated by the pre-trained CNN according to a frame of the target video; convolving each of the sub-feature maps with one of a plurality of adaptive convolution kernels, respectively, to output a plurality of second feature maps with improved adaptability; training, frame by frame, the adaptive convolution kernels.

Methods and systems for CNN network adaption and object online tracking

In a convolutional neural network (CNN) using an encoder-decoder structure for image segmentation, a multi-scale context aggregation module receives an encoded final-stage feature map from the encoder, and sequentially aggregates multi-scale contexts of this feature map from a global scale to a local scale to strengthen semantic relationships of contexts of different scales to improve segmentation accuracy. The multi-scale contexts are obtained by computing atrous convolution on the feature map for different dilation rates. To reduce computation, a channel-wise feature selection (CFS) module is used in the decoder to merge two input feature maps. Each feature map is processed by a global pooling layer followed by a fully connected layer or a 1&#xd7;1 convolutional layer to select channels of high activation. By subsequent channel-wise multiplication and elementwise summation, only channels with high activation in both feature maps are preserved and enhanced in the merged feature map.

Method for image segmentation using CNN

Disclosed are methods for object tracking. In an example, the method comprises: determining a region of interest (ROI) in a first frame of a video sequences; feeding the determined ROI forward through a first CNN (convolutional network) to obtain a plurality of first feature maps in a higher layer of the CNN and a plurality of second feature maps in a lower layer of the first CNN; selecting a plurality of feature maps from the first and second feature maps, respectively; predicting, based on the selected first and second feature maps, two target heat maps indicating a target location for said objects in the current frame, respectively; and estimating, based on the two predicated target heat maps, a final target location for the object in the current frame.

Systems and methods for object tracking

A visual attention detector includes a feature extraction unit configured to extract a spatiotemporal feature from a local region in a video; a hashing unit configured to convert a spatiotemporal feature value for the local region into a hash value, and to select a training value mapped to the hash using a hash table; and an attention measure determining unit configured to determine an attention measure on the basis of the distance between a spatiotemporal feature value for the local region and the selected training value such that the larger the distance the larger the attention measure.

Visual attention detector and visual attention detection method

A saliency information acquisition device has a local saliency acquisition unit configured to calculate a saliency measure for each pixel in an input image on the basis of information obtained from a local region surrounding each pixel, a candidate-region setting unit configured to set a plurality of candidate regions in the input image, a global saliency acquisition unit configured to calculate a saliency measure for each candidate region in the plurality of candidate regions on the basis of information including a local saliency feature representing an attribute of the saliency measure for each pixel within a candidate region, and a global feature representing an attribute of the candidate regions in relation to the entire input image, and an integration unit configured to combine the saliency measure for each candidate region in the plurality of candidate regions obtained by the global saliency acquisition unit to generate saliency information.

Saliency information acquisition device and saliency information acquisition method

An image processing apparatus has an image acquisition unit that acquires an image that is to be subjected to processing, a learning sample extraction unit that extracts data of a plurality of learning samples from the image, a classifier learning unit that performs learning of a plurality of classifiers using the plurality of learning samples, a strong classifier generation unit that generates a strong classifier by combining the plurality of learned classifiers, and a saliency map generation unit that generates a saliency map of the image using the strong classifier.

Image processing apparatus and image processing method

A collaborative distance metric leaning method and apparatus for visual tracking utilizes a distance metric to match the target with the best candidate. A collaborative distance metric learning algorithm is used for visual tracking by fusing two distance metrics learnt for different representations of the target. To further improve the performance of the trained distance metrics, a sample selection mechanism is also used that exploits the intrinsic structure of dense sampling.

Collaborative distance metric learning for method and apparatus visual tracking

An image processing device and a method performed by a computer, the image processing device comprising: a processing unit configured to operate as an image acquisition unit configured to acquire an image; a similarity map generator configured to calculate, with a region constructed with one or a plurality of pixels in the image as a constituent unit, a first similarity map based on a first algorithm, the first similarity map representing a degree of similarity between the region and a marginal region of the image, and calculate a second similarity map based on a second algorithm, the second similarity map representing a degree of similarity between the region and the marginal region of the image; and a saliency map generator configured to integrate the first similarity map and the second similarity map to generate a saliency map.

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Dalian, China

Huchuan Lu