Multi-object tracking with a knowledge-based, autonomous adaptation of the tracking modeling level

The invention proposes a method for object and object configuration tracking based on sensory input data, the method comprising the steps of:

The present invention describes a sensor-based system for tracking objects and object configurations that adapts the complexity of the involved modeling level. The adaptation occurs autonomously by a situation-dependent adjustment and by incorporating knowledge in form of prototypical relationship graphs between tracking models.

Generally a tracking system in the present invention processes sensory data andgenerates control signals in order to make a sensor track one or more targets in the input field of the sensor(s), orindicates the region of one or more tracked targets in the input field of the sensor(s).

BACKGROUND OF THE INVENTION

Target tracking such as e.g. visual (video-based) target tracking is a topic of major relevance to a large series of computer vision domains involving monitoring, surveillance, guidance, obstacle avoidance and scene interpretation. The application domains include diverse fields such as radar control, traffic monitoring or vision-based robotics.

Tracking an object involves model descriptions of 1) how the object parameters evolve over time and as well as 2) how the estimated state of an object can be related to a sensory measurement. These two models have to be tailored to the specific object that should be tracked and its dynamics, e.g. by indicating that the object behaves in a ballistic fashion according to Newtonian physics and e.g. a sensory measurement using a characteristic, known object color.

For domain-specific applications, a single, fixed description of the involved models is sufficient. In a situation involving complex visual scenes, however, a system is needed that allows a dynamic switching and adaptation of the involved models. An example is e.g. a situation of a bouncing ball target, which moves in a ballistic fashion while falling down but rebounds when hitting the floor, making a different motion model necessary.

The current way of dealing with such situations is by introducing mixture models [1] which are treated probabilistically, allowing the tracking system to give more weight to those models that best fit with the sensory observations.

The drawback of currently available tracking models is that all the possible single models used for the mixture have to be directly integrated into the tracking process from the start, and that they have to be evaluated simultaneously.

Nevertheless, if one considers that the complexity level of objects that can be tracked is not fixed, that objects can be arranged into trackable object configurations, and that this may occur hierarchically by arranging object configurations into even larger, trackable ensembles, the potential space of tracking models becomes combinatorially large. No previously fixed mixture of tracking models can then be devised to cover the entire range of possible tracking models.

PRIOR ART

Tracking is the estimation of the dynamic parameters of objects from a sensory input. Well-known examples are position tracking for moving objects; in this case, the goal is not to loose an object and to accurately estimate its 2D or 3D position, velocity, etc.

A typical tracking process together with its tracking models is shown inFIG. 1.

As shown inFIG. 1, an object1or an object configuration2is sensed by a sensor device3generating sensory input for the tracking system4.

Under “object configuration”2it is to be understood that objects, such as for example a car body5and wheels6,6′ carrying out different movements (pure displacement in case of the car body5and a combination of displacement and rotation in case of the wheels6,6′) are grouped together as they carry out one common trackable movement, which in the present example is the displacement of the car as such.

The tracking process and tracking system shown inFIG. 1essentially comprises a confirmation module7as well as a prediction model8.

The tracking system4is provided with tracking models9. As already explained, the tracking models9are descriptions of how the object parameters evolve over time as well as how the estimated state of an object can be related to a sensory input.

As shown inFIG. 1, the tracking involves two steps, termed in the following prediction and confirmation or measurement, and which are defined by their corresponding prediction and confirmation or measurement models, also shown inFIG. 1. In the prediction step, the estimated parameters of the object represented by an internal state are extrapolated into the future by a chosen dynamical model that describes how the state is expected to change in time, usually yielding a series of hypothetical future states. In the confirmation step, the series of hypothetical future states are compared for compatibility with the sensory input, and those states that provided a good prediction of the future state then dominate the estimation of the current state.

In short, a tracking process imposes a constrained search strategy for the dynamical state estimation of a target, where the hypotheses are generated and confirmed according to prior knowledge about the tracked objects in form of preset models and the quality of the models has a large impact on the quality of the tracking process. It is therefore crucial to find the right models for a given tracking task and situation. Along the same line of argumentation, it is also of importance to find the right level of granularity and abstraction of the models. A tracker could e.g. estimate the wheels of a car each separately. But this is probably not the best level of abstraction for describing the dynamics, since it is advantageous to have an internal prediction model that indicates that the wheels movement is generated by a common cause, in this case a moving car to which the wheels are attached to.

No single tracking model exists that fits equally well to all situations and all levels of granularity. Furthermore, in dynamic scenes, the movement of the object varies and so do the optimal tracking models. For optimal and efficient tracking, an autonomous adjustment of the description models involved in the tracking process is therefore necessary.

The probabilistic main stream of researchers refers to tracking on a level of dynamic Bayesian inference (e.g. [1]), describing it as a stochastic state estimation problem in discrete time. Here, a state vector x of the target contains parameters of the object like its position, velocity and acceleration and so on. The Bayesian methods first predict new states of the target (i.e., the expected states at the next timestep, together with their occurrence probability) using a state prediction model. Then they use a measurement of the state of the target to evaluate (i.e. either confirm or reject) the predicted states of the target.

The exemplary Bayesian formulas below describe this two-step behavior of current tracking methods
p(xk|Z1:k-1)=∫p(xk|xk-1)p(xk-1|Z1:k-1)dxk
p(xk|Z1:k)˜p(Zk|xk)p(xk|Z1:k-1)
based on the states x (with their temporal indices) and the sensory measurements Z (Z with indices 1:k representing all measurements from time-steps 1 through k, and ˜ meaning “proportional to”). The first line gives the probability of a new, predicted state x given all past measurements (the prediction step), whereas the second line expresses that the probability of the current state is a multiplicative (“Bayesian”) combination of the so-called measurement likelihood with the predicted state (the confirmation step).

There exist several ways of implementing the Bayesian formulas, depending on the linearity nonlinearity of the involved steps, like Kalman filtering [2], or sequential Monte Carlo estimation using particle filters [3].

In state-of-the-art methods, increasing the level of complexity of the tracker models involves an extension of the state vector and therefore indirectly of the related models for the prediction and confirmation steps. An example of how this is approached for object configurations is given in [4]. It also involves a decision of what the maximal state vector can be, incorporating the knowledge directly into it.

A further prior art approach is given by tracking algorithms based on multiple switching dynamic models [5] or IMM's (interacting multiple models) [6]. These are hybrid filtering methods which evaluate several tracking models (e.g. prediction models and/or likelihood models) in parallel and include an internal switching dynamics between the models themselves.

Previous tracking algorithms have also made use of a multitude of adaptation methods to improve the tracking process. E.g. in [7], a system is described that learns and adapts its internal representation to intrinsic as well as extrinsic changes based on a time-varying Eigenbasis description of the appearance of the tracked object. However, in the invention proposed here we present as a novel aspect an adaptation at the level of switching between different prediction resp. confirmation models, which provides a way of incorporating higher-level model-, object- and context knowledge in form of a corresponding hierarchical knowledge basis.

Finally, graphical representations are heavily used in computer vision, tracking (e.g. [8]) and probabilistic modeling, mainly for the low-level sensory decomposition of the visual. The invention extends them for the purpose of describing relationships between modeling processes. In this invention, these involve nodes that represent the different models and undirected or directed edges to describe neighborhood relations, possible transitions or dependencies between models. Hierarchical representations deserve special consideration of tracking models as e.g. representable by directed acyclic graphs (DAG).

U.S. Pat. No. 6,295,367B1 (reference 8) discloses a system and method for tracking movement of objects in a scene from a stream of video frames using first and second correspondence graphs. A first correspondence graph, called an object correspondence graph, is formed comprising a plurality of nodes representing region clusters in the scene which are hypotheses of objects to be tracked, and a plurality of tracks. Each track comprises an ordered sequence of nodes in consecutive video frames that represents a track segment of an object through the scene. A second correspondence graph, called a track correspondence graph, is created, comprising a plurality of nodes, each node corresponding to at least one track in the first correspondence graph. A track comprising an ordered sequence of nodes in the second correspondence graph represents the path of an object through the scene. Tracking information for objects, such as persons, in the scene, is accumulated based on the first correspondence graph and second correspondence graph.

OBJECT OF THE INVENTION

It is the object of the invention to at least alleviate the introduced tracking problems. The object is achieved by means of the features of the independent claims. The dependent claims develop further the central idea of the present invention.

The invention proposes a method for an autonomous, adaptive adjustment of the model complexity needed to track an object or an object configuration. One aspect of the invention resided in the idea that there is background knowledge in the system about how the level of complexity of a tracking model can be increased (e.g. by imposing further dynamical constraints, or by letting an object participate in a object configuration) or decreased (e.g. by loosening dynamical constraints or by releasing an object from an object configuration), and that, depending on the tracking success of higher-level or lower-level tracking models, these are switched accordingly.

In summary, the invention allows to adapt the tracking process dynamically, during run-time, to the appropriate abstraction level to yield a better tracking performance. It also allows to trade off tracking model complexity, accuracy and computational costs by choosing the appropriate modeling level along a hierarchy of models. Adaptive configuration tracking can exploit the hierarchical structure to adapt to the right complexity level in terms of the number of properties of parts and subparts that constitute a tracked object. In addition, such a tracking system can adjust very effectively to extrinsic changes in a tracked object's dynamic behavior, e.g. when a falling ball changes abruptly its trajectory as soon as it hits a rigid surface, demanding a different prediction model of the object dynamics.

DETAILED DESCRIPTION OF THE INVENTION

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 database11about tracking models9and their relationship combined with a short-term sensory memory12for the multi-object tracking system (STM,FIG. 2B).

The long-term memory11is a unit storing relationships between tracking models9, while the short term memory has the data for the tracking process itself.

The long-term memory database11contains, for each tracking model9, 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 memory12contains 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 memory11) needed for the target state estimation. The confirmation step directly relates the internal representations of the tracked objects with the objects1in the outer world. Furthermore, the tracking models9may need additional information about the world/context, this is then contained by additional short-term and long-term context memories10(in the example, only a short term context memory10is shown, although a corresponding memory can be present also for the long term memory11).

The tracking system has two working loop modules, i.e. an inner loop module15and an outer loop module14as shown inFIG. 3. Both loops14,15are provided with sensory input3.

The outer loop module14decides on basic tracker recruitment and thus comprises a basic recruitment module16: It detects interesting parts in the supplied sensory input3which are not yet covered by already tracked objects (i.e., tracked objects with representations in the short-term memory12) and initializes basic trackers17for these parts (objects).

The basic trackers17are nodes18of the tracking model graph representation (FIG. 2) in the long-term memory11which directly involve sensory measurements for tracking state confirmation. It is also the task of the outer loop14to decide on the lifetime of tracked objects in the short term memory12, and to release29the tracking of objects that do not receive sufficient sensory support in the confirmation phase any more. The reasons for tracker release29can 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 field3.

The inner loop module15comprises an autonomous complexity adjustment module19for the tracking models in the short-term memory12.

This is achieved by (i) scanning the tracking model graph from the long-term memory11to 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 adjustment19can 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 memory12would 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 adjustment19, during operation each tracked object or object configuration retains a memory link to the current and past tracking model(s) from the long-term memory11(FIG. 2, links20between LTM11and STM12). This enables the exploration of the long-term memory graph for possible alternative tracking models. E.g., tracking models9that 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 module21), since different prediction and confirmation models are used during the tracking process.

During the process of tracking model complexity adjustment19, 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 module21), 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).

EXAMPLE

A specific example for a combined 2D/3D tracking system is shown inFIG. 4. A stereo video camera30,31(being an example for streaming sensors) supplies “binocular” 2D video data to the tracking system and such comprises a “left” video camera30and a “right” video camera31. The tracking system (i.e. the entire system shown inFIG. 3) processes these supplied video data30,31.

The long-term memory11contains tracking model descriptions of trackers working in 2D and in 3D, i.e. a 3D tracking model32, a left camera 2D tracking model33and a right camera 2D tracking model34. The trackers33,34, 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 tracker32contains a ballistic prediction model working in 3D world coordinates. Its measurement model is based on the result of two lower-level 2D trackers33,34resp. their 2D positions, with each 2D tracker33,34working on a separate camera30,31.

The context memory (10inFIG. 2) 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 model32then assumes that results from the left and right 2D tracking models33,34(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 inFIG. 4. Similarly the predicted states of the 3D tracking model32are projected downwards (in the tracking model graph structure) towards the left and right 2D tracking models33,34, constraining the 2D regions where these trackers33,34should expect an object. Finally, the left and right 2D trackers33,34seek the confirmation of their state by applying their measurement model on the left and right camera images30,31, respectively.

During operation, at first, the basic tracker recruitment module (16inFIG. 3) sets the 2D trackers33,34on identifiable objects, independently for the left and right cameras30,31. From the long-term memory graph (11inFIG. 2) of tracking models, the system infers that a tracked object from the left camera30can be combined with a tracked object from the right camera31. It then tries to initialize (17inFIG. 3) a tracked 3D object with its corresponding 3D tracking model. The 3D tracker32then makes use of the result of the already initiated 2D trackers33,34, using their state estimations as basis for its own measurements and constraining the predictions of the 2D trackers33,34. These can work in mixed mode, combining their own 2D prediction model(s) with the prediction delivered from the 3D tracker32. In a sense, the 3D tracker32is 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.

PRIOR ART REFERENCES