Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
  • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
  • G06V10/771—Feature selection, e.g. selecting representative features from a multi-dimensional feature space
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F18/00—Pattern recognition
  • G06F18/20—Analysing
  • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
  • G06F18/211—Selection of the most significant subset of features
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F18/00—Pattern recognition
  • G06F18/20—Analysing
  • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
  • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
  • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
  • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
  • G06V10/7747—Organisation of the process, e.g. bagging or boosting

Definitions

• the present invention is directed to a method for combining boosted classifiers for efficient multi-class object detection, and more particularly, to a system and method for training a detection system that identifies multi-class objects using boosted classifiers.
• Boosting techniques are particularly effective for detecting a single object class.
• Many detection applications require multiple object class detection in order to be effective.
• An example of such an application is vehicle detection where separate object classes may be defined for vehicles, trucks, pedestrians and traffic signs.
• Another example of a detection application that requires multiple object classes is people detection. Particularly, if the people are in motion, it is more effective to define people sub-classes based on the difference poses or actions of the people. For example, such subclasses could include sitting, standing and walking.
• the object detection is solved by evaluating the strong classifier h on candidate image patches x e X .
• ⁇ is a threshold allowing the user to balance false alarm and miss detection rate.
• An optimal selection of the weak classifiers h t and a proper weighting ⁇ t is obtained from an AdaBoost training algorithm.
• AdaBoost to train one individual ensemble of weak classifiers H (/) and weights ⁇ (/ for each class /, that is,
• a more efficient multi-class detection method and system is devised by a joint design of key components including feature and classifier design of individual detectors.
• a method for training a system for detecting multi-class objects in an image or a video sequence is described.
• a common ensemble of weak classifiers for a set of object classes is identified.
• a separate weighting scheme is adapted for the ensemble of weak classifiers.
• the present invention is also directed to a method for detecting objects of multiple classes in an image or a video sequence.
• Each class is assigned a detector that is implemented by a weighted combination of weak classifiers such that all of the detectors are based on a common ensemble of weak classifiers. Then weights are individually set for each class.
• FIG. 1 is a system block diagram of a system for detecting and tracking multi-class objects in accordance with the present invention.
• FIG. 2 illustrates a multi-class object detection framework in accordance with the present invention.
• the present invention is directed to a system and method for combining boosted classifiers for efficient multi-class object detection.
• the present invention is also directed to a method for training classifiers for multi-class object detection.
• the present invention can be used in a multitude of applications that require the detection of different objects.
• Such a system may, for example, be used for surveillance applications, such as for detecting and tracking a person or facial features.
• the present invention could also be used to detect and track objects on an assembly line. Other applications could be created for detecting and tracking human organs for medical applications. It is to be understood by those skilled in the art that the present invention may be used in other environments as well. Another environment in which the present invention would be useful is in the detection and tracking of vehicles. In addition to detecting different types of vehicles, there is also sometimes a need for detecting pedestrians, traffic signs and other vehicle-environment related objects. For purposes of describing the present invention, it will be described in the on-road obstacle detection environment.
• FIG. 1 illustrates a block diagram of a system for implementing the present invention.
• One or more cameras 102 are used to capture images of a road and its surroundings. As would be expected with a typical road image, the image includes background images, such as buildings, trees, and houses, and vehicles driving on the road.
• the images are communicated to a processor 104 which computes confidence scores using the component classifiers in a database 106.
• the images are analyzed in accordance with the present invention to identify different classes of objects. Examples of such objects include cars, trucks, pedestrians and traffic signs.
• Once an object is identified at a sufficient confidence level, the object is identified via an output device 108.
• the output device 108 provides an output signal which communicates to the user the presence of one or more objects.
• the output signal may be an audible signal or other type of warning signal.
• the output device 308 may also include a display for viewing the detected objects.
• the display provides a view of the images taken by the cameras 302 which may then be enhanced to indicate objects that have been detected and which are being tracked.
• the present invention is directed to a number of training techniques which are used to identify and select a number of weak classifiers which are then stored in database 106 and used to detect the various objects.
• a premise of the present invention is an underlying principle that all L detectors share a common set of features or weak classifiers.
• each individual strong classifier is adapted to its individual set of weights ⁇ (1) posterior to the weak classifier selection.
• individual weighting there is only a minimal increase in the computation complexity compared to single class detection.
• the majority of the complexity derived in the computations of Equations (3) and (4) of the prior art arise from the evaluation of the weak classifiers and not the weighting.
• FIG. 2 illustrates a multi-class object detection framework in accordance with the present invention.
• Training of the system refers to the selection of a common set of weak classifiers H and the adaptation of individual weights ⁇ (1) .
• the remaining open issue is the selection of the common weak classifier ensemble H.
• three different techniques are described for selecting H.
• the first technique assumes that one distinguished class l 0 exists.
• the distinguished class can be, for instance, a class with a high occurrence of objects in the scene.
• the training set of class / 0 is used to select a common ensemble
• AdaBoost AdaBoost
• the candidate weak classifiers are taken solely from H.
• An example of an application where this technique might be used is a manufacturing application. Since the predominant object being detected might be an item on an assembly line the weak classifiers associated with that object would be used as the training set.
• a second technique for creating a training set is used when the assignment of one distinguished class is not reasonable. In such an instance, a more symmetric procedure would be appropriate.
• a common classifier H is trained based on the union of all training samples.
• each individual classifier / is fine-tuned by adjusting ⁇ ⁇ with respect to H and the positive training samples solely from class I.
• H is optimized in order to discriminate all positive objects from the negatives.
• the individual detection problem can be specialized by adapting the respective impact of the individual weak classifiers.
• facial detection uses classes that are made up of different facial poses. However, many of the classifiers for the poses are similar (e.g., eyes, nose, and mouth).
• vehicle detection uses classes that are made up of different vehicles. In each case, normally certain features such as the edges and corners of the vehicle are looked at.
• a third technique obtains H by collecting the most appropriate weak classifiers for each object class I.
• individual sets of weak classifiers are selected in a first training step.
• the final set H is obtained from a combination of ⁇ H 1) ,.--,H (L) ⁇ .
• One approach is to use the union
• This technique is best used for applications that require a distribution of data resources.
• the application may also detect, traffic signs and pedestrians. Additional objects that may be detected include road lanes, guard rails and background objects like trees, houses and other buildings.
• each of the classes uses weak classifiers that are distinct from the other classes. However, there still may be some common weak classifiers among all of the classes.
• the framework shown in FIG. 2 can be extended to be applicable for multi-class classification.
• the classifier assigns the most probable class label
• the present invention uses a set of boosted weak classifiers that is common within all individual class detectors and thus has be to computed only once. A possible major loss in detection accuracy by this restriction is prevented by a posterior re-training of the weak classifier weights, individually to each object class to reduce its misclassification error.
• the training complexity of the present invention does not increase significantly compared to the prior art approaches since re-adjustment of the individual class weights with AdaBoost is based only on H and thus on a much small set of weak classifier candidates.

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• 2005
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