Self-adaptive cascade classifier training method based on online learning

The invention discloses a self-adaptive cascade classifier training method based on online learning, which comprises the following steps: (1), preparing a training sample set with a small quantity of samples, and training an initial cascade classifier HC(x) in a cascade classifier algorithm; (2), using the HC(x) for traversal of image frames to be detected, extracting areas with sizes identical to the sizes of the training samples one by one, calculating a feature value set, classifying the areas with the initial cascade classifier, and judging whether the areas are target areas, thereby completing target detection; (3) tracking the detected targets in a particle filtering algorithm, verifying the target detection results through tracking, marking detection with errors as a negative sample for online learning, obtaining different attitudes of real targets through tracking and extracting a positive sample for online learning; and (4) carrying out online training and updating for the initial ...

Method for counting passenger flow of buses in real time

The invention provides a method for counting passenger flow of buses in real time, which adopts means of machine learning, multi-target detection and tracking, target behavior analysis and the like, and belongs to the technical field of pattern recognition. The method comprises the following concrete steps: detecting passenger targets by analyzing shape information and texture information of passenger heads in video images, wherein a column diagram in a gradient direction is used for representing the shape information, and a column diagram in a local binary mode is used for representing the texture information; then, accurately positioning passengers by adopting the target tracking policy combining gray level cross-correlation matching tracking and mean translation algorithm searching tracking; and finally, judging the behavior characteristics of the passengers by analyzing the moving tracks of the passengers, thereby accurately counting the passenger flow of buses. The practice shows that ...

Self-adaptive cascade classifier training method based on online learning

The invention discloses a self-adaptive cascade classifier training method based on online learning, which comprises the following steps: (1), preparing a training sample set with a small quantity of samples, and training an initial cascade classifier HC(x) in a cascade classifier algorithm; (2), using the HC(x) for traversal of image frames to be detected, extracting areas with sizes identical to the sizes of the training samples one by one, calculating a feature value set, classifying the areas with the initial cascade classifier, and judging whether the areas are target areas, thereby completing target detection; (3) tracking the detected targets in a particle filtering algorithm, verifying the target detection results through tracking, marking detection with errors as a negative sample for online learning, obtaining different attitudes of real targets through tracking and extracting a positive sample for online learning; and (4) carrying out online training and updating for the initial ...

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Real-time bus passenger flow volume statistical method

The invention provides a real-time bus passenger flow volume statistical method based on a target prior distribution, which adopts the methods of target detection, target tracking, target behavioral analysis and the like, and belongs to the technical field of pattern recognition. The method concretely comprises the following steps: utilizing the target prior grayscale to do statistics of the back-projection operation of a histogram on an input video image, and then performing the treatment of difference, binaryzation, wave filtering, connected domain marking and the like on a back-projection image to realize the target detection; adopting the grayscale to interrelate, match and track and a mean translation algorithm to search and track so as to realize the accurate positioning of the target; and finally analyzing the motion trail of the target to judge the get on or off of passengers, and realizing the passenger flow volume statistics. The invention can provide fine-grained reliable basis ...

Target detection method

The invention discloses a target detection method based on weak linear regression trees with incremental dimensions, which comprises the following steps: (1) preparing a training sample set (xi, yi), wherein i is equal to 1,..., N, xi expresses the characteristic values combination of training samples, yi expresses the types of samples, N expresses the number of the training samples, and N is a natural number; (2) initializing the weight of each training sample to be as shown in the specification, wherein t is a natural number and is equal to 1 in the process of initialization. (3) circularly computing the sample set for T times to acquire a linear regression tree every time to be used as a weak classifier, and then, synthesizing the T numbered weak classifiers acquired by circularly computing for T times into a strong classifier, wherein T is a natural number; and (4) classifying each region in digital images by using the strong classifier to judge whether the region is a target region ...

Method for counting passenger flow of buses in real time

The invention provides a method for counting passenger flow of buses in real time, which adopts means of machine learning, multi-target detection and tracking, target behavior analysis and the like, and belongs to the technical field of pattern recognition. The method comprises the following concrete steps: detecting passenger targets by analyzing shape information and texture information of passenger heads in video images, wherein a column diagram in a gradient direction is used for representing the shape information, and a column diagram in a local binary mode is used for representing the texture information; then, accurately positioning passengers by adopting the target tracking policy combining gray level cross-correlation matching tracking and mean translation algorithm searching tracking; and finally, judging the behavior characteristics of the passengers by analyzing the moving tracks of the passengers, thereby accurately counting the passenger flow of buses. The practice showsthat ...

Real-time bus passenger flow volume statistical method

The invention provides a real-time bus passenger flow volume statistical method based on a target prior distribution, which adopts the methods of target detection, target tracking, target behavioral analysis and the like, and belongs to the technical field of pattern recognition. The method concretely comprises the following steps: utilizing the target prior grayscale to do statistics of the back-projection operation of a histogram on an input video image, and then performing the treatment of difference, binaryzation, wave filtering, connected domain marking and the like on a back-projection image to realize the target detection; adopting the grayscale to interrelate, match and track and a mean translation algorithm to search and track so as to realize the accurate positioning of the target; and finally analyzing the motion trail of the target to judge the get on or off of passengers, and realizing the passenger flow volume statistics. The invention can provide fine-grained reliable basis ...