EP-4738251-A1 - CONTROLLING A LIST OF INACTIVE TRACKS USED FOR RE-IDENTIFICATION IN AN OBJECT TRACKING SYSTEM

EP4738251A1EP 4738251 A1EP4738251 A1EP 4738251A1EP-4738251-A1

Abstract

The present disclosure relates to a method (600), system and software for controlling a list of inactive tracks in an object tracking system. The techniques described includes obtaining (S604) the false positive rate, FPR, of a re-identification model using a decision threshold, and obtaining (S606) an acceptable FPR of the tracking system. The re-identification model is used to match new detections with inactive tracks. The techniques include determining (S608) one or more terminal conditions for deleting inactive tracks based on the acceptable and model FPRs. If a first inactive track meets a terminal condition, it is deleted (S610) from the list.

Inventors

Colliander, Christian
Hasselberg, Emanuel S
ERIKSSON, Jonatan
DANIELSSON, NICLAS
ROSELL, NIKLAS
Ärlebäck, Richard
Laross, Sarah

Assignees

Axis AB

Dates

Publication Date: 20260506
Application Date: 20241030

Claims (15)

A method (600, 700, 800) for controlling a list (110) of inactive tracks (302, 304, 306, 308) used for re-identification in an object tracking system (100) comprising an object detector (102), the object tracking systems tracking objects in a scene; the object tracking system comprising a re-identification model (112) used when attempting to associate object detection data (106) received from the object detector with an inactive track from the list, the re-identification model being a metric learning model trained on object detection data, the method comprising: obtaining (S602), using a decision threshold (206) of the re-identification model, a false positive rate of the re-identification model; obtaining (S604) an acceptable false positive rate of the object tracking system; determining (S606), using the acceptable false positive rate of the object tracking system and the false positive rate of the re-identification model, one or more terminal conditions for deleting an inactive track from the list; determining (S608) that a first inactive track fulfils a terminal condition from the one or more terminal conditions; and deleting (S610) the inactive track from the list.
The method of claim 1, wherein each inactive track in the list is associated with a counter (303, 305, 307, 309) which indicates a number of unsuccessful association attempts for the inactive track, wherein the method further comprises: determining (S702), using the acceptable false positive rate of the object tracking system and the false positive rate of the re-identification model, a threshold number of association attempts of each the inactive track in the list; wherein a first terminal condition of the one or more terminal conditions comprises the counter exceeding the threshold number of association attempts obtaining (S704) first object detection data from the object detector; and evaluating (S706) whether the first object detection data is associated with the first inactive track from the list, wherein upon the object detection data is not associated with the first inactive track from the list, increasing the counter associated with the first inactive track.
The method of claim 2, further comprising: obtaining a plurality of object detection data from the object detector, the plurality of object detection data being associated with a same image frame of a video stream depicting the scene; for each object detection data of the plurality of object detection data, evaluating whether the object detection data is associated with the first inactive track from the list, wherein upon the object detection data is not associated with the first inactive track from the list, increasing the counter associated with the first inactive track; wherein the step of determining that a first inactive track fulfils the first terminal condition from the one or more terminal conditions is performed after all object detection data of the plurality of object detection data has been evaluated.
The method of any one of claims 2-3, wherein upon the first object detection data is associated with the first inactive track from the list, deleting the first inactive track from the list.
The method of any one of claims 2-4, wherein the threshold number is determined by solving the equation TPR model N = TPR system wherein TPR model indicates the true positive rate of the re-identification model, wherein the TPR system indicates 1 minus the acceptable false positive rate of the object tracking system and, wherein the threshold number is determined using N .
The method of any one of claims 2-5, wherein each inactive track in the list is associated with a location data indicating a location in a scene where the inactive track was determined to be inactive by the object tracking system, the method further comprising: determining first location data from the obtained object detection data; selecting a subset of inactive tracks from the list based on location data from the first object detection data and the location data associated with each of the inactive tracks in the list; and for each inactive track in the subset of inactive tracks, evaluating whether the object detection data is associated with the inactive track, wherein upon the object detection data is not associated with the inactive track, increasing the counter associated with the inactive track.
The method of any one of claims 2-6, further comprising: obtaining a maximum number of inactive tracks in the list: sorting the list of inactive tracks according to their associated counters; truncating the list to comprise the maximum number of inactive tracks.
The method of any one of claims 1-7, wherein each inactive track in the list is associated with a timer (502, 504, 506, 508) indicating a time span since the inactive track was added to the list, wherein the method further comprises: determining (S802) a current number of objects, p , in the scene; estimating (S804) how many attempts, r , to associate object detection data received from the object detector with an inactive track from the list is performed by the object tracking system per inactive track and time unit; and determining (S806) a threshold time span in time units using p , r , the acceptable false positive rate of the object tracking system and the false positive rate of the re-identification model, wherein a second terminal condition of the one or more terminal conditions comprises a time span of an inactive track exceeding the threshold time span.
The method of claim 8, wherein the threshold time span, t , in time units is determined by solving the equation TPR model r * p * t = TPR system , or wherein the threshold time span, t , in time units is determined by solving the equation TPR model R ( p ) * t = TPR system , wherein R corresponds to a total number of association attempts that is performed by the object tracking system per time unit and is a function of p .
The method of claim 8, further comprising: estimating a change, p', of the number of objects in the scene per time unit using p and historical data indicating counts of objects in the scene at a plurality of points in time; wherein the threshold time span, t , in time units is determined by solving the equation TPR model R ( p ) * t = TPR system wherein R corresponds to a total number of association attempts that is performed by the object tracking system per time unit is a function of p '.
The method of any one of claims 8-10, further comprising: obtaining a maximum number of inactive tracks in the list: sorting the list of inactive tracks according to the time span indicated by their associated timers; and truncating the list to comprise the maximum number of inactive tracks.
The method of any one of claims 1-11, further comprising: obtaining, from a user of the object detection system, an updated acceptable false positive rate of the object tracking system; and updating, using the updated acceptable false positive rate of the object tracking system, at least one of the threshold number of attempts and the threshold time span.
A non-transitory computer-readable storage medium having stored thereon instructions for implementing the method according to any one of claims 1-12 when executed on one or more devices having processing capabilities.
An object tracking system comprising an object detector, the object tracking systems tracking objects in a scene; the object tracking system comprising a re-identification model used when attempting to associate object detection data received from the object detector with an inactive track from the list, the re-identification model being a metric learning model trained on object detection data, the object tracking system configured to control a list of inactive tracks used for re-identification by: obtaining, using a decision threshold of the re-identification model, a false positive rate of the re-identification model; obtaining an acceptable false positive rate of the object tracking system; determining, using the acceptable false positive rate of the object tracking system and the false positive rate of the re-identification model, one or more terminal conditions for deleting an inactive track from the list; determining that a first inactive track fulfils a terminal condition from the one or more terminal conditions; and deleting the inactive track from the list.
The obj ect tracking system of claim 14, connected to a camera capturing a video stream depicting the scene.

Description

Technical Field The present invention relates to object tracking and in particular to a method, system and software for controlling a list of inactive tracks used for re-identification in an object tracking system. Background Object tracking systems are crucial for applications such as surveillance and autonomous vehicles, where continuous monitoring of objects in a scene is required. These systems use video feeds and algorithms to track objects across multiple frames, forming what is called an "object track." This track is built from a series of associated detections of the same object. However, tracking may be interrupted due to occlusions, objects moving out of view, or missed detections. When this happens, the object track becomes inactive, and these inactive tracks are stored in a memory or "inactive track gallery." If the object reappears, it can be re-identified using the gallery of inactive tracks, thus preventing the creation of redundant tracks for the same object. In general, "re-identification" (ReID) refers to the process of correctly identifying and associating a previously detected object with a new detection of the same object. New detections may be matched, using ReID, with inactive tracks (representing previously detected objects) by comparing feature vectors that represent the appearance or visual characteristics of the detects objects. These feature vectors are typically outputted by a convolutional neural network (CNN) trained to produce vectors that are similar for images containing the same object and dissimilar for images of different objects. A decision model assesses the feature similarity between new detections and inactive tracks by comparing the feature vectors associated with the new detections and the inactive track. A similarity score may be computed (e.g., Euclidean distance or cosine similarity), and used to take the decision whether the new detection should be matched with the inactive track or not. However, the accuracy of the decision model decreases as the number of inactive tracks increases. For example, a CNN may have only a 70% accuracy in matching a detection in a gallery of 100 inactive tracks, but this improves to 90% when there are only five tracks. To manage this, a time limit (e.g., 5 seconds) may be used, discarding inactive tracks that have been inactive longer than the time limit to keep the gallery small and maintain accuracy. This method works well in scenes with a moderate object density, where the number of inactive tracks remains manageable. However, in scenes with very few objects, the time limit may be unnecessarily restrictive, and retaining inactive tracks longer could improve the chances of successful re-identification without overloading the system. Conversely, in scenes with many objects, inactive tracks may need to be discarded more quickly to prevent the gallery from growing too large, which would otherwise reduce the accuracy of the decision model. There is thus a need for improvements in this context. Summary In view of the above, solving or at least reducing one or several of the drawbacks discussed above would be beneficial, as set forth in the attached independent patent claims. According to a first aspect of the present disclosure, there is provided a method for controlling a list of inactive tracks used for re-identification in an object tracking system comprising an object detector, the object tracking systems tracking objects in a scene; the object tracking system comprising a re-identification model used when attempting to associate object detection data received from the object detector with an inactive track from the list, the re-identification model being a metric learning model trained on object detection data, the method comprising: obtaining, using a decision threshold of the re-identification model, a false positive rate of the re-identification model; obtaining an acceptable false positive rate of the object tracking system; determining, using the acceptable false positive rate of the object tracking system and the false positive rate of the re-identification model, one or more terminal conditions for deleting an inactive track from the list; determining that a first inactive track fulfils a terminal condition from the one or more terminal conditions; and deleting the inactive track from the list. Advantageously, using the techniques described herein, the static time limit for how long an inactive track can stay in the list of inactive tracks used for re-identification is replaced by one or more terminal conditions for when to discard inactive tracks. The one or more terminal conditions are based on the acceptable false positive rate of the object tracking system and the false positive rate of the re-identification model (ReID model). This in turn makes the time that an inactive track stays in the list dependent on how crowdy the scene is and/or the complexity of the tracked scene. Using the techniques of the present disclosur