Abstract: A method for tracking an object in a scene is provided, including receiving a first image frame of a first image stream capturing a scene; detecting or tracking a first object in the first image frame, and determining that the first object is in a first part of the first image frame; determining, using object detection, that the first object belongs to a first object class; determining, based on multiple values of a tracking parameter each specific for different parts of the first image frame and for different object classes, a first value of the tracking parameter for the first part of the first image and for the first object class, and tracking the first object in a second image frame of the first image stream subsequent to the first image frame, based on the first value of the tracking parameter.

Abstract: A method, a device and a non-transitory computer-readable storage medium for setting a confidence threshold for objects detected in a region of a captured scene based on historical confidence scores for objects detected in that region.

Abstract: A method for feature extraction of detected objects, comprising the steps of: receiving a plurality of images, each depicting an object detected by the object detecting application; concatenating the plurality of images into a composite image according to a grid pattern; feeding the composite image through a convolutional neural network (CNN) trained for feature extraction, wherein each convolutional layer of the CNN is configured to, while convolving input data to the convolutional layer using a convolutional filter: determine a currently convolved image of the plurality of images by determining a centre coordinate of a subset of the input data currently covered by the convolutional filter, and mapping the centre coordinate to the grid pattern; and selectively nullifying all weights of the convolutional filter that cover input data derived from any of the plurality of images not being the currently convolved image.

Abstract: A computer-implemented method of tracking objects in a video sequence of a scene comprises determining a location of a sink in the scene where objects exit the scene and a location of a source where objects enter the scene; tracking a first object moving in the scene using a re-identification algorithm, wherein the first object is associated with a re-identification threshold of the re-identification algorithm; detecting that the first object has exited the scene at the sink; and responsive to detecting that the first object has exited the scene at the sink, adjusting the re-identification threshold associated with the first object such that a probability that the re-identification algorithm re-identifies a second object, entering the scene at the source after the first object has exited the scene at the sink, as the first object is reduced.

Abstract: A computer-implemented method of tracking objects in a video sequence of a scene comprises determining a location of a sink in the scene where objects exit the scene and a location of a source where objects enter the scene; tracking a first object moving in the scene using a re-identification algorithm, wherein the first object is associated with a re-identification threshold of the re-identification algorithm; detecting that the first object has exited the scene at the sink; and responsive to detecting that the first object has exited the scene at the sink, adjusting the re-identification threshold associated with the first object such that a probability that the re-identification algorithm re-identifies a second object, entering the scene at the source after the first object has exited the scene at the sink, as the first object is reduced.

Abstract: A method for prioritizing feature extraction for object re-identification in an object tracking application. Region of interests (ROI) for object feature extraction is determined based on motion areas in the image frame. Each object detected in an image frame and which is at least partly overlapping with a ROI is associated with the ROI. A list of candidate objects for feature extraction is determined by, for each ROI associated with two or more objects: adding each object of the two or more objects that is not overlapping with any of the other objects among the two or more objects with more than a threshold amount. From the list of candidate objects, at least one object is selected, and image data of the image frame depicting the selected object is used for determining a feature vector for the selected object.

Abstract: A method for feature extraction of detected objects, comprising the steps of: receiving a plurality of images, each depicting an object detected by the object detecting application; concatenating the plurality of images into a composite image according to a grid pattern; feeding the composite image through a convolutional neural network (CNN) trained for feature extraction, wherein each convolutional layer of the CNN is configured to, while convolving input data to the convolutional layer using a convolutional filter: determine a currently convolved image of the plurality of images by determining a centre coordinate of a subset of the input data currently covered by the convolutional filter, and mapping the centre coordinate to the grid pattern; and selectively nullifying all weights of the convolutional filter that cover input data derived from any of the plurality of images not being the currently convolved image.

Abstract: A method for prioritizing feature extraction for object re-identification in an object tracking application. Region of interests (ROI) for object feature extraction is determined based on motion areas in the image frame. Each object detected in an image frame and which is at least partly overlapping with a ROI is associated with the ROI. A list of candidate objects for feature extraction is determined by, for each ROI associated with two or more objects: adding each object of the two or more objects that is not overlapping with any of the other objects among the two or more objects with more than a threshold amount. From the list of candidate objects, at least one object is selected, and image data of the image frame depicting the selected object is used for determining a feature vector for the selected object.

Abstract: A method, a device and a non-transitory computer-readable storage medium for setting a confidence threshold for objects detected in a region of a captured scene based on historical confidence scores for objects detected in that region.

Abstract: A method for tracking an object in a scene is provided, including receiving a first image frame of a first image stream capturing a scene; detecting or tracking a first object in the first image frame, and determining that the first object is in a first part of the first image frame; determining, using object detection, that the first object belongs to a first object class; determining, based on multiple values of a tracking parameter each specific for different parts of the first image frame and for different object classes, a first value of the tracking parameter for the first part of the first image and for the first object class, and tracking the first object in a second image frame of the first image stream subsequent to the first image frame, based on the first value of the tracking parameter.