Patent Application: US-95112710-A

Abstract:
the invention proposes a method for object and object configuration tracking based on sensory input data , the method comprising the steps of : basic recruiting : detecting interesting parts in sensory input data which are not yet covered by already tracked objects and incrementally initializing basic tracking models for these parts to continuously estimate their states , tracking model complexity adjustment : testing , during runtime more complex and more simple prediction and / or measurement models on the tracked objects , and basic release : releasing trackers from parts of the sensory data where the tracker prediction and measurement processes do not get sufficient sensory support for some time .

Description:
the present invention proposes a method and a system for object and object configuration tracking that makes use of an autonomous , situation - dependent adjustment of the tracker modeling level for optimal tracking . the adjustment occurs by means of mixed model evaluation incorporating several tracking models from neighboring complexity levels , and the knowledge that enables the selection of suitable tracking models is given by a system - inherent graphical representation of the tracking models and their relationships . the invention proposes a long - term memory knowledge database 11 about tracking models 9 and their relationship combined with a short - term sensory memory 12 for the multi - object tracking system ( stm , fig2 b ). the long - term memory 11 is a unit storing relationships between tracking models 9 , while the short term memory has the data for the tracking process itself . the long - term memory database 11 contains , for each tracking model 9 , information of the prediction and / or confirmation models that should be used during the tracking process . the multi - object tracking system in the short term memory 12 contains state information about the currently tracked objects or object configurations and executes the prediction and confirmation steps ( defined by the corresponding tracking model from the long - term memory 11 ) needed for the target state estimation . the confirmation step directly relates the internal representations of the tracked objects with the objects 1 in the outer world . furthermore , the tracking models 9 may need additional information about the world / context , this is then contained by additional short - term and long - term context memories 10 ( in the example , only a short term context memory 10 is shown , although a corresponding memory can be present also for the long term memory 11 ). the tracking system has two working loop modules , i . e . an inner loop module 15 and an outer loop module 14 as shown in fig3 . both loops 14 , 15 are provided with sensory input 3 . the outer loop module 14 decides on basic tracker recruitment and thus comprises a basic recruitment module 16 : it detects interesting parts in the supplied sensory input 3 which are not yet covered by already tracked objects ( i . e ., tracked objects with representations in the short - term memory 12 ) and initializes basic trackers 17 for these parts ( objects ). the basic trackers 17 are nodes 18 of the tracking model graph representation ( fig2 ) in the long - term memory 11 which directly involve sensory measurements for tracking state confirmation . it is also the task of the outer loop 14 to decide on the lifetime of tracked objects in the short term memory 12 , and to release 29 the tracking of objects that do not receive sufficient sensory support in the confirmation phase any more . the reasons for tracker release 29 can be of many kinds and may be caused by internal or external events , such as a wrong choice of tracker models or simply the disappearance of an object from the sensory input field 3 . the inner loop module 15 comprises an autonomous complexity adjustment module 19 for the tracking models in the short - term memory 12 . this is achieved by ( i ) scanning the tracking model graph from the long - term memory 11 to select alternative tracking model candidates related to the current ones ( in terms of graph connectivity ), ( ii ) the performance evaluation of the alternative tracking model candidates and ( iii ) the decision if one of the alternative models will be used to continue tracking a given object . the complexity adjustment 19 can be achieved by modification of the prediction and / or confirmation models , e . g . by using a model for 3d motion constrained to run perpendicular to a given support surface such as it is the case for cars on a street , instead of an unconstrained 3d motion model . it also may include the combination of several , previously independently tracked objects into an object configuration that is then tracked as a single compound , imposing constraints on the possible positions of each constituting object . a complexity decrease of tracking models in the short - term memory 12 would e . g . be given by a less complex / less constrained motion model or by the splitting up of an object configuration tracker into several single object trackers . for the purpose of autonomous complexity adjustment 19 , during operation each tracked object or object configuration retains a memory link to the current and past tracking model ( s ) from the long - term memory 11 ( fig2 , links 20 between ltm 11 and stm 12 ). this enables the exploration of the long - term memory graph for possible alternative tracking models . e . g ., tracking models 9 that are neighbors in the graph to the currently used tracking model can be evaluated and the tracking model ( s ) of an object can be changed . the changed memory link then has consequences on the tracked object performance ( evaluated by a tracking performance evaluation module 21 ), since different prediction and confirmation models are used during the tracking process . during the process of tracking model complexity adjustment 19 , it is often sensible to allow tracking models to coexist during some time . in the system according to the invention this means that the two tracking models are executed in parallel , in a mixed mode . in a first variant , these run independently from each other and are evaluated separately at each time - step , e . g . in terms of their probabilistic properties such as the confidence of the object state estimation . in a second variant , the two models can be mixed into a joint probabilistic framework ( see prior art mention of multiple switching dynamic models for tracking ), but again leading to an evaluation of the performance of each model for each time - step . after a temporal integration of the evaluation , a decision is then taken on which tracking model ( s ) to use . however , if tracking performance is sufficiently high ( as assessed by module 21 ), it is often desirable to continue tracking objects using a mixed model , since with such a method temporal weaknesses of one model can be rapidly compensated by other models . in this case , the long - term memory graph of tracking models provides valuable information on which models should be mixed ( e . g . models that are close to each other in terms of graph relationships ). a specific example for a combined 2d / 3d tracking system is shown in fig4 . a stereo video camera 30 , 31 ( being an example for streaming sensors ) supplies “ binocular ” 2d video data to the tracking system and such comprises a “ left ” video camera 30 and a “ right ” video camera 31 . the tracking system ( i . e . the entire system shown in fig3 ) processes these supplied video data 30 , 31 . the long - term memory 11 contains tracking model descriptions of trackers working in 2d and in 3d , i . e . a 3d tracking model 32 , a left camera 2d tracking model 33 and a right camera 2d tracking model 34 . the trackers 33 , 34 , working in 2d contain a simple , 2d ballistic prediction model to describe the position of objects on a camera image , and also apply their measurement models directly on these images to confirm the expected positions . the 3d tracker 32 contains a ballistic prediction model working in 3d world coordinates . its measurement model is based on the result of two lower - level 2d trackers 33 , 34 resp . their 2d positions , with each 2d tracker 33 , 34 working on a separate camera 30 , 31 . the context memory ( 10 in fig2 ) in this case contains information about the position and orientation of the cameras in the world coordinate system needed by the 3d tracker . for the sake of a simple explanation , it is assumed that the cameras are arranged like in a binocular system , and call them “ left ” and “ right ”. the 3d tracking model 32 then assumes that results from the left and right 2d tracking models 33 , 34 ( the estimated left and right 2d camera positions ) are delivered as sensory input and used for the higher - level tracker state confirmation step , as can be seen in fig4 . similarly the predicted states of the 3d tracking model 32 are projected downwards ( in the tracking model graph structure ) towards the left and right 2d tracking models 33 , 34 , constraining the 2d regions where these trackers 33 , 34 should expect an object . finally , the left and right 2d trackers 33 , 34 seek the confirmation of their state by applying their measurement model on the left and right camera images 30 , 31 , respectively . during operation , at first , the basic tracker recruitment module ( 16 in fig3 ) sets the 2d trackers 33 , 34 on identifiable objects , independently for the left and right cameras 30 , 31 . from the long - term memory graph ( 11 in fig2 ) of tracking models , the system infers that a tracked object from the left camera 30 can be combined with a tracked object from the right camera 31 . it then tries to initialize ( 17 in fig3 ) a tracked 3d object with its corresponding 3d tracking model . the 3d tracker 32 then makes use of the result of the already initiated 2d trackers 33 , 34 , using their state estimations as basis for its own measurements and constraining the predictions of the 2d trackers 33 , 34 . these can work in mixed mode , combining their own 2d prediction model ( s ) with the prediction delivered from the 3d tracker 32 . in a sense , the 3d tracker 32 is both a configuration tracker ( since it uses a combination of two objects ) as well as a higher level tracking model , since it now uses a true 3d model for state prediction and confirmation . 1 . arulampalam , s ., maskell , s ., gordon , n . : a tutorial on particle filters for online nonlinear / non - 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