Abstract: Multi-mode video event indexing includes determining a quality of object distinctiveness with respect to images from a video stream input. A high-quality analytic mode is selected from multiple modes and applied to video input images via a hardware device to determine object activity within the video input images if the determined level of detected quality of object distinctiveness meets a threshold level of quality, else a low-quality analytic mode is selected and applied to the video input images via a hardware device to determine object activity within the video input images, wherein the low-quality analytic mode is different from the high-quality analytic mode.

Abstract: Techniques for detecting one or more events are provided. The techniques include identifying one or more segments in a video sequence as one or more candidates for one or more events by a temporal ordering of the one or more candidates, and analyzing one or more motion patterns of the one or more candidates to detect the one or more events.

Abstract: Transaction units of video data and transaction data captured from different checkout lanes are prioritized as a function of lane priority values of respective ones of the different checkout lanes from which the transaction units are acquired. Each of the checkout lanes has a different lane priority value. The individual transaction units are processed in the prioritized processing order to automatically detect irregular activities indicated by the transaction unit video and the transaction data of the processed individual transaction units.

Abstract: Automatic object retrieval from input video is based on learned, complementary detectors created for each of a plurality of different motionlet clusters. The motionlet clusters are partitioned from a dataset of training vehicle images as a function of determining that vehicles within each of the scenes of the images in each cluster share similar two-dimensional motion direction attributes within their scenes. To train the complementary detectors, a first detector is trained on motion blobs of vehicle objects detected and collected within each of the training dataset vehicle images within the motionlet cluster via a background modeling process; a second detector is trained on each of the training dataset vehicle images within the motionlet cluster that have motion blobs of the vehicle objects but are misclassified by the first detector; and the training repeats until all of the training dataset vehicle images have been eliminated as false positives or correctly classified.

Abstract: Local models learned from anomaly detection are used to rank detected anomalies. The local models include image feature values extracted from an image field of video image data with respect to different predefined spatial and temporal local units, wherein anomaly results are determined by failures to fit to applied anomaly detection module local models. Image features values extracted from the image field local units associated with anomaly results are normalized, and image feature values extracted from the image field local units are clustered. Weights for anomaly results are learned as a function of the relations of the normalized extracted image feature values to the clustered image feature values. The normalized values are multiplied by the learned weights to generate ranking values to rank the anomalies.

Abstract: Field of view overlap among multiple cameras are automatically determined as a function of the temporal overlap of object tracks determined within their fields-of-view. Object tracks with the highest similarity value are assigned into pairs, and portions of the assigned object track pairs having a temporally overlapping period of time are determined. Scene entry points are determined from object locations on the tracks at a beginning of the temporally overlapping period of time, and scene exit points from object locations at an ending of the temporally overlapping period of time. Boundary lines for the overlapping fields-of-view portions within the corresponding camera fields-of-view are defined as a function of the determined entry and exit points in their respective fields-of-view.

Abstract: Objects within two-dimensional video data are modeled by three-dimensional models as a function of object type and motion through manually calibrating a two-dimensional image to the three spatial dimensions of a three-dimensional modeling cube. Calibrated three-dimensional locations of an object in motion in the two-dimensional image field of view of a video data input are determined and used to determine a heading direction of the object as a function of the camera calibration and determined movement between the determined three-dimensional locations. The two-dimensional object image is replaced in the video data input with an object-type three-dimensional polygonal model having a projected bounding box that best matches a bounding box of an image blob, the model oriented in the determined heading direction. The bounding box of the replacing model is then scaled to fit the object image blob bounding box, and rendered with extracted image features.

Abstract: Foreground object image features are extracted from input video via application of a background subtraction mask, and optical flow image features from a region of the input video image data defined by the extracted foreground object image features. If estimated movement features indicate that the underlying object is in motion, a dominant moving direction of the underlying object is determined. If the dominant moving direction is parallel to an orientation of the second, crossed thoroughfare, an event alarm indicating that a static object is blocking travel on the crossing second thoroughfare is not generated. If the estimated movement features indicate that the underlying object is static, or that its determined dominant moving direction is not parallel to the second thoroughfare, an appearance of the foreground object region is determined and a static-ness timer run while the foreground object region comprises the extracted foreground object image features.

Abstract: Long-term understanding of background modeling includes determining first and second dimension gradient model derivatives of image brightness data of an image pixel along respective dimensions of two-dimensional, single channel image brightness data of a static image scene. The determined gradients are averaged with previous determined gradients of the image pixels, and with gradients of neighboring pixels as a function of their respective distances to the image pixel, the averaging generating averaged pixel gradient models for each of a plurality of pixels of the video image data of the static image scene that each have mean values and weight values. Background models for the static image scene are constructed as a function of the averaged pixel gradients and weights, wherein the background model pixels are represented by averaged pixel gradient models having similar orientation and magnitude and weights meeting a threshold weight requirement.

Abstract: Multi-mode video event indexing includes determining a quality of object distinctiveness with respect to images from a video stream input. A high-quality analytic mode is selected from multiple modes and applied to video input images via a hardware device to determine object activity within the video input images if the determined level of detected quality of object distinctiveness meets a threshold level of quality, else a low-quality analytic mode is selected and applied to the video input images via a hardware device to determine object activity within the video input images, wherein the low-quality analytic mode is different from the high-quality analytic mode.

Abstract: Multi-mode video event indexing includes determining a quality of object distinctiveness with respect to images from a video stream input. A high-quality analytic mode is selected from multiple modes and applied to video input images via a hardware device to determine object activity within the video input images if the determined level of detected quality of object distinctiveness meets a threshold level of quality, else a low-quality analytic mode is selected and applied to the video input images via a hardware device to determine object activity within the video input images, wherein the low-quality analytic mode is different from the high-quality analytic mode.

Abstract: Transaction units of video data and transaction data captured from different checkout lanes are prioritized as a function of lane priority values of respective ones of the different checkout lanes from which the transaction units are acquired. Each of the checkout lanes has a different lane priority value. The individual transaction units are processed in the prioritized processing order to automatically detect irregular activities indicated by the transaction unit video and the transaction data of the processed individual transaction units.

Abstract: Field of view overlap among multiple cameras are automatically determined as a function of the temporal overlap of object tracks determined within their fields-of-view. Object tracks with the highest similarity value are assigned into pairs, and portions of the assigned object track pairs having a temporally overlapping period of time are determined. Scene entry points are determined from object locations on the tracks at a beginning of the temporally overlapping period of time, and scene exit points from object locations at an ending of the temporally overlapping period of time. Boundary lines for the overlapping fields-of-view portions within the corresponding camera fields-of-view are defined as a function of the determined entry and exit points in their respective fields-of-view.

Abstract: Objects within two-dimensional video data are modeled by three-dimensional models as a function of object type and motion through manually calibrating a two-dimensional image to the three spatial dimensions of a three-dimensional modeling cube. Calibrated three-dimensional locations of an object in motion in the two-dimensional image field of view of a video data input are determined and used to determine a heading direction of the object as a function of the camera calibration and determined movement between the determined three-dimensional locations. The two-dimensional object image is replaced in the video data input with an object-type three-dimensional polygonal model having a projected bounding box that best matches a bounding box of an image blob, the model oriented in the determined heading direction. The bounding box of the replacing model is then scaled to fit the object image blob bounding box, and rendered with extracted image features.

Abstract: Local models learned from anomaly detection are used to rank detected anomalies. The local models include image feature values extracted from an image field of video image data with respect to different predefined spatial and temporal local units, wherein anomaly results are determined by failures to fit to applied anomaly detection module local models. Image features values extracted from the image field local units associated with anomaly results are normalized, and image feature values extracted from the image field local units are clustered. Weights for anomaly results are learned as a function of the relations of the normalized extracted image feature values to the clustered image feature values. The normalized values are multiplied by the learned weights to generate ranking values to rank the anomalies.

Abstract: Machine logic that pre-processes and post-processes images for visual object detection by performing the following steps: receiving a set of image(s); filtering the set of image(s) using a set of multimodal integral filter(s), thereby removing at least a portion of the set of image(s) and resulting in a filtered set of image(s); performing object detection on the filtered set of image(s) to generate a set of object-detected image(s); assembling a first plurality of object-detected image(s) from the set of object-detected image(s); and upon assembling the first plurality of object-detected image(s), performing non-maximum suppression on the assembled first plurality of object-detected image(s).

Abstract: Multi-mode video event indexing includes determining a quality of object distinctiveness with respect to images from a video stream input. A high-quality analytic mode is selected from multiple modes and applied to video input images via a hardware device to determine object activity within the video input images if the determined level of detected quality of object distinctiveness meets a threshold level of quality, else a low-quality analytic mode is selected and applied to the video input images via a hardware device to determine object activity within the video input images, wherein the low-quality analytic mode is different from the high-quality analytic mode.

Abstract: Long-term understanding of background modeling includes determining first and second dimension gradient model derivatives of image brightness data of an image pixel along respective dimensions of two-dimensional, single channel image brightness data of a static image scene. The determined gradients are averaged with previous determined gradients of the image pixels, and with gradients of neighboring pixels as a function of their respective distances to the image pixel, the averaging generating averaged pixel gradient models for each of a plurality of pixels of the video image data of the static image scene that each have mean values and weight values. Background models for the static image scene are constructed as a function of the averaged pixel gradients and weights, wherein the background model pixels are represented by averaged pixel gradient models having similar orientation and magnitude and weights meeting a threshold weight requirement.

Abstract: Multi-mode video event indexing includes determining a quality of object distinctiveness with respect to images from a video stream input. A high-quality analytic mode is selected from multiple modes and applied to video input images via a hardware device to determine object activity within the video input images if the determined level of detected quality of object distinctiveness meets a threshold level of quality, else a low-quality analytic mode is selected and applied to the video input images via a hardware device to determine object activity within the video input images, wherein the low-quality analytic mode is different from the high-quality analytic mode.

Abstract: Foreground feature data and motion feature data is determined for frames of video data acquired from a train track area region of interest. The frames are labeled as “train present” if the determined foreground feature data value meets a threshold value, else as “train absent; and as “motion present” if the motion feature data meets a motion threshold, else as “static.” The labels are used to classify segments of the video data comprising groups of consecutive video frames, namely as within a “no train present” segment for groups with “train absent” and “static” labels; within a “train present and in transition” segment for groups “train present” and “motion present” labels; and within a “train present and stopped” segment for groups with “train present” and “static” labels. The presence or motion state of a train at a time of inquiry is thereby determined from the respective segment classification.