Abstract: Techniques are provided for recognition of activity in a sequence of video image frames that include depth information. A methodology embodying the techniques includes segmenting each of the received image frames into a multiple windows and generating spatio-temporal image cells from groupings of windows from a selected sub-sequence of the frames. The method also includes calculating a four dimensional (4D) optical flow vector for each of the pixels of each of the image cells and calculating a three dimensional (3D) angular representation from each of the optical flow vectors. The method further includes generating a classification feature for each of the image cells based on a histogram of the 3D angular representations of the pixels in that image cell. The classification features are then provided to a recognition classifier configured to recognize the type of activity depicted in the video sequence, based on the generated classification features.

Abstract: Techniques are provided for recognition of activity in a sequence of video image frames that include depth information. A methodology embodying the techniques includes segmenting each of the received image frames into a multiple windows and generating spatio-temporal image cells from groupings of windows from a selected sub-sequence of the frames. The method also includes calculating a four dimensional (4D) optical flow vector for each of the pixels of each of the image cells and calculating a three dimensional (3D) angular representation from each of the optical flow vectors. The method further includes generating a classification feature for each of the image cells based on a histogram of the 3D angular representations of the pixels in that image cell. The classification features are then provided to a recognition classifier configured to recognize the type of activity depicted in the video sequence, based on the generated classification features.

Abstract: Techniques are provided for recognition of activity in a sequence of video image frames that include depth information. A methodology embodying the techniques includes segmenting each of the received image frames into a multiple windows and generating spatio-temporal image cells from groupings of windows from a selected sub-sequence of the frames. The method also includes calculating a four dimensional (4D) optical flow vector for each of the pixels of each of the image cells and calculating a three dimensional (3D) angular representation from each of the optical flow vectors. The method further includes generating a classification feature for each of the image cells based on a histogram of the 3D angular representations of the pixels in that image cell. The classification features are then provided to a recognition classifier configured to recognize the type of activity depicted in the video sequence, based on the generated classification features.

Abstract: The present disclosure describes a non-learning based process and apparatus for detecting humans in an image. This may include receiving an image that has pixel distance information from a camera and using that to determine a height of the pixel above a ground surface. One or more regions may then be identified that may include a head and shoulders of an individual in the image. A multiple threshold technique may be used to remove some background regions, and a mean-shift technique used to find the local highest regions that may be combination of head and shoulders of the person. In embodiments, the view angle and/or the height of the camera may not be fixed.

Abstract: Techniques are provided for recognition of activity in a sequence of video image frames that include depth information. A methodology embodying the techniques includes segmenting each of the received image frames into a multiple windows and generating spatio-temporal image cells from groupings of windows from a selected sub-sequence of the frames. The method also includes calculating a four dimensional (4D) optical flow vector for each of the pixels of each of the image cells and calculating a three dimensional (3D) angular representation from each of the optical flow vectors. The method further includes generating a classification feature for each of the image cells based on a histogram of the 3D angular representations of the pixels in that image cell. The classification features are then provided to a recognition classifier configured to recognize the type of activity depicted in the video sequence, based on the generated classification features.

Abstract: The present disclosure describes a non-learning based process and apparatus for detecting humans in an image. This may include receiving an image that has pixel distance information from a camera and using that to determine a height of the pixel above a ground surface. One or more regions may then be identified that may include a head and shoulders of an individual in the image. A multiple threshold technique may be used to remove some background regions, and a mean-shift technique used to find the local highest regions that may be combination of head and shoulders of the person. In embodiments, the view angle and/or the height of the camera may not be fixed.

Abstract: A method and a device for objects counting in image processing includes acquiring the depth image of any one frame; detecting objects according to the depth image; associating the identical object in different frames to form a trajectory; and determining the number of objects according to the number of trajectories. The devices include an acquisition module for acquiring the depth image of any one frame; a detection module for detecting objects according to the depth image; an association module for associating the identical object in different frames to form a trajectory; a determining module for determining the number of objects according to the number of trajectories. The objects are detected according to the depth image. The identical object in different frames is associated to form a trajectory and the number of objects is determined according to the number of trajectories.

Abstract: A method and device for extracting color features, relating to the field of image processing includes converting an original image into sub-images corresponding to channels in a color space, dividing the sub-images into a plurality of cells with identical size, and calculating the color histograms of each of the plurality of cells. A cell and neighboring cells thereof are defined as the effective scope of the cell, and a feature vector of the cell is determined according to a degree of similarity of the color histograms of every two neighboring cells in the effective scope. The feature vectors of the cells in the sub-images are concatenated to obtain feature vectors of the sub-images, and a feature vector of the original image is obtained according to the feature vectors of the sub-images.

Abstract: An object search method, a search verification method, and apparatuses thereof, pertain to the field of video surveillance technologies. The object search method includes: acquiring an object image for search and a designated region of the object image for search, and calculating local feature points in the designated region of the object image for search; searching in a pre-constructed index set for indexes matching the local feature points in the designated region; and acquiring object images corresponding to the detected indexes, and using the acquired object images as detected object images in a video. In this way, object search is implemented by using local regions of an image, the application range of the object search is extended, and accuracy of the search result is improved.

Abstract: A method and a device for objects counting in image processing includes acquiring the depth image of any one frame; detecting objects according to the depth image; associating the identical object in different frames to form a trajectory; and determining the number of objects according to the number of trajectories. The devices include an acquisition module for acquiring the depth image of any one frame; a detection module for detecting objects according to the depth image; an association module for associating the identical object in different frames to form a trajectory; a determining module for determining the number of objects according to the number of trajectories. The objects are detected according to the depth image. The identical object in different frames is associated to form a trajectory and the number of objects is determined according to the number of trajectories.

Abstract: A method and a device for training a pose classifier and an object classifier, and a method and a device for objection detection, relating to the field of image processing are provided. The object detection method includes acquiring input image samples; performing pose estimation processing on said input image sample according to said pose classifier; and performing object detection on the processed input image sample according to said pose classifier to acquire the location information of the object, wherein said object is an object with joints. Objects in different poses can be detected and therefore the object hit rate is increased.

Abstract: A method and device for extracting color features, relating to the field of image processing includes converting an original image into sub-images corresponding to channels in a color space, dividing the sub-images into a plurality of cells with identical size, and calculating the color histograms of each of the plurality of cells. A cell and neighboring cells thereof are defined as the effective scope of the cell, and a feature vector of the cell is determined according to a degree of similarity of the color histograms of every two neighboring cells in the effective scope. The feature vectors of the cells in the sub-images are concatenated to obtain feature vectors of the sub-images, and a feature vector of the original image is obtained according to the feature vectors of the sub-images.

Abstract: A method and a device for people group detection, relating to the field of image processing includes acquiring at least one corner and foreground region in video data; computing, according to said corner and foreground region, to obtain at least one cluster; constructing at least one region of people group according to said cluster. The device includes an acquisition module, a clustering module, and a construction module. Automatic people group detection is realized that is independent of the location detection of people and can be applied to any scene including complicated ones, and thus user demands can be better satisfied.