Classifying an object in a video frame

A non-transitory computer-readable storage medium storing a set of instructions to cause a computer to perform steps for selectively tracking an object appearing in a video sequence comprising a plurality of frames, each frame comprising a plurality of pixels, the steps comprising:

• (a) identifying a first object in a first frame and associating therewith a first status parameter indicative of a non-tracking condition;
• (b) identifying a plurality of candidate objects in a subsequent frame and determining whether there is a correspondence between any one of the candidate objects and the first object,
• (c) in the event of correspondence between a candidate object and the first object, repeating step (b) for that candidate object over subsequent frames until a predetermined number of sequential correspondences are identified;
• (d) changing the first status parameter to a second status parameter when said predetermined number of sequential correspondences are identified;
• and
• (e) in response to the change from first to second status parameter, recording the intra-frame position of said object for subsequent frames;
• wherein correspondence is determined in step (b) by:
• calculating a first similarity score between each candidate object and the first object using a first cost function, calculating a second similarity score using a second cost function which is different from the first cost function, grouping the calculated similarity scores in accordance with the status parameter of the first object, analysing each group in a predetermined order and selecting the closest match between the candidate objects and the first object.

A video processing system for selectively tracking an object appearing in a video sequence comprising a plurality of frames, each frame comprising a plurality of pixels, the system being arranged, in use, to:

• (a) identify a first object in a first frame and associating therewith a first status parameter indicative of a non-tracking condition;
• (b) identify a plurality of candidate objects in a subsequent frame and determining whether there is a correspondence between any one of the candidate objects and the first object;
• (c) in the event of correspondence between a candidate object and the first object, repeat step (b) for that candidate object over subsequent frames until a predetermined number of sequential correspondences are identified;
• (d) change the first status parameter to a second status parameter when said predetermined number of sequential correspondences are identified;
• (e) in response to the change from first to second status parameter, record the intra-frame position of said object for subsequent frames;
• and
• calculate a first similarity score between each candidate object and the first object using a first cost function, calculate a second similarity score using a second cost function which is different from the first cost function, group the calculated similarity scores in accordance with the status parameter of the first object, analyze each group in a predetermined order and select the closest match between the candidate objects and the first object.

A method of classifying an object in a video sequence comprising a plurality of frames, the method comprising:

• (a) identifying a first object in a first frame and associating therewith a status parameter having one of a plurality of predetermined states, each state having a different transition rule associated therewith;
• (b) identifying at least one candidate object in a subsequent frame;
• (c) comparing the at least one candidate object with the first object to determine if there is a correspondence therebetween;
• and
• (d) updating the status parameter of the first object in accordance with its associated transition rule, said transition rule indicating which of the predetermined states the status parameter should be transited to dependent on whether a correspondence was identified in step (c)
• wherein correspondence in step (c) is determined by:
• calculating a first similarity score between each candidate object and the first object using a first cost function, calculating a second similarity score using a second cost function which is different from the first cost function, grouping the calculated similarity scores in accordance with the status parameter of the first object, analyzing each group in a predetermined order and selecting the closest match between the candidate objects and the first object.

A video processing apparatus comprising:

• an input for receiving frames of a video sequence;
• an object queue arranged to store data representing objects identified in one or more frames and, associated with each object, a status parameter representing one of a plurality of predetermined states;
• a video analysis unit arranged to receive a frame having one or more candidate objects therein, and to determine whether the or each candidate object matches an object in the object queue;
• and
• a state transition controller arranged to update the status parameter of each object in the object queue in accordance with a transition rule corresponding to the state of the object's status parameter, said transition rule indicating which of the predetermined states the status parameter should be transited to;
• wherein the video analysis unit is further arranged to:
• calculate a first similarity score between each candidate object and the first object using a first cost function, calculate a second similarity score using a second cost function which is different from the first cost function, group the calculated similarity scores in accordance with the status parameter of the first object, analyse each group in a predetermined order and select the closest match between the candidate objects and the first object.

A method for tracking an object appearing in a video sequence comprising a plurality of frames, each frame comprising a plurality of pixels, the method comprising:

• (a) identifying a first object in a first frame and associating therewith a first status parameter indicative of a non-tracking condition;
• (b) identifying a candidate object in a subsequent frame and determining whether there is a correspondence between the candidate object and the first object;
• (c) in the event of correspondence, repeating steps (b) and (c) for further subsequent frames until a predetermined number of sequential correspondences are identified;
• (d) changing the first status parameter to a second status parameter when said sequential correspondences are identified;
• and
• (e) in response to the change from first to second status parameter, recording the intra-frame position of said object for subsequent frames;
• wherein, step (d) comprises changing the first status parameter to the second status parameter when (i) said sequential correspondences are identified and (ii) position characteristics of the object meet a set of predefined criteria;
• and
• wherein step (ii) comprises assigning a motion factor ζm to the first object based on its position characteristics over a plurality of frames, and classifying said first object as meeting the predefined criteria if the motion factor is above a predetermined threshold Tζ.

A method for tracking an object appearing in a video sequence comprising a plurality of frames, each frame comprising a plurality of pixels, the method comprising:

• (a) identifying a first object in a first frame and associating therewith a first status parameter indicative of a non-tracking condition;
• (b) identifying a candidate object in a subsequent frame and determining whether there is a correspondence between the candidate object and the first object;
• (c) in the event of correspondence, repeating steps (b) and (c) for further subsequent frames until a predetermined number of sequential correspondences are identified;
• (d) changing the first status parameter to a second status parameter when said sequential correspondences are identified;
• (e) in response to the change from first to second status parameter, recording the intra-frame position of said object for subsequent frames;
• and
• providing a first set of data representing appearance features fio of the first object, and extracting, for the candidate object in the subsequent frame, a second set of data representing appearance features fib of that object, step (b) comprising combining the first set of appearance data with the second set of appearance data in a cost function Cob thereby to generate a numerical parameter indicating the degree of correspondence between the first object and the or each new candidate region.