Honda Motor Co., Ltd.

David Ross

Ming-Hsuan Yang

Jongwoo Lim

Takahiro Ohashi

Ruei-Sung Lin

A system and a method are disclosed for adaptive probabilistic tracking of an object within a motion video. The method utilizes a time-varying Eigenbasis and dynamic, observation and inference models. The Eigenbasis serves as a model of the target object. The dynamic model represents the motion of the object and defines possible locations of the target based upon previous locations. The observation model provides a measure of the distance of an observation of the object relative to the current Eigenbasis. The inference model predicts the most likely location of the object based upon past and present observations. The method is effective with or without training samples. A computer-based system provides a means for implementing the method. The effectiveness of the system and method are demonstrated through simulation.

Adaptive probabilistic visual tracking with incremental subspace update

The face detection system and method attempts classification of a test image before performing all of the kernel evaluations. Many subimages are not faces and should be relatively easy to identify as such. Thus, the SVM classifier try to discard non-face images using as few kernel evaluations as possible using a cascade SVM classification. In the first stage, a score is computed for the first two support vectors, and the score is compared to a threshold. If the score is below the threshold value, the subimage is classified as not a face. If the score is above the threshold value, the cascade SVM classification function continues to apply more complicated decision rules, each time doubling the number of kernel evaluations, classifying the image as a non-face (and thus terminating the process) as soon as the test image fails to satisfy one of the decision rules. Finally, if the subimage has satisfied all intermediary decision rules, and has now reached the point at which all support vectors must be considered, the original decision function is applied. Satisfying this final rule, and all intermediary rules, is the only way for a test image to garner a positive (face) classification.

Face recognition system

A system and a method are disclosed for an adaptive discriminative generative model with a probabilistic interpretation. As applied to visual tracking, the discriminative generative model separates the target object from the background more accurately and efficiently than conventional methods. A computationally efficient algorithm constantly updates the discriminative model over time. The discriminative generative model adapts to accommodate dynamic appearance variations of the target and background. Experiments show that the discriminative generative model effectively tracks target objects undergoing large pose and lighting changes.

Adaptive discriminative generative model and application to visual tracking

The advantage of the present invention is to appropriately detect the object. The object detection apparatus in the present invention has a plurality of cameras to determine the distance to the objects, a distance determination unit to determine the distance therein, a histogram generation unit to specify the frequency of the pixels against the distances to the pixels, an object distance determination unit that determines the most likely distance, a probability mapping unit that provides the probabilities of the pixels based on the difference of the distance, a kernel detection unit that determines a kernel region as a group of the pixels, a periphery detection unit that determines a peripheral region as a group of the pixels, selected from the pixels being close to the kernel region and an object specifying unit that specifies the object region where the object is present with a predetermined probability.

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Toronto, Canada

David Ross