System and Method For Single-Loop Vehicle Classification Using Inductive Loop Vehicle Signatures
First Claim
1. A system for classifying vehicles, the system comprising:
- a non-transitory computer readable memory configured to store a classification library having a plurality of entries, each entry in the library having a vehicle classification;
a sequence generating module operably connected to an inductive loop sensor for receiving a vehicle signature therefrom, and configured to convert the vehicle signature into a sequence of numbers;
an input set generating module operably connected to the sequence generating module for receiving the sequence, and configured to identify peaks in the sequence and to generate an input set, the input set comprising the sequence, a quantity of peaks in the sequence, a location of a first peak in the sequence, and a value of the first peak;
a classification module operably connected to the memory for accessing the classification library, operably connected to the input set generating module for receiving the input set, and configured to perform K-nearest neighbor (KNN) classification with the classification library on the input set thereby determining an estimated vehicle classification for the input set; and
an output module configured to output the estimated vehicle classification.
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Abstract
Single loop inductive sensors are widely deployed in infrastructure for traffic data collection, however, these loops currently provide little more than vehicle detection. A system and method are provided that enable single loop inductive sensors to be used for vehicle classification (e.g., identification as motorcycle, passenger car, bus, etc.). Classification may be done using the Federal Highway Administration'"'"'s 13 class system. Initially a signature library is built from vehicle signatures with known classifications. Vehicle signature waveforms of unknown classification obtained from inductive loop sensors are analyzed to identify specific features in the waveform including the number of “peaks”, the first peak location and its magnitude. A classifier (e.g., K-nearest neighbor) uses a representation of the vehicle signature and the features to determine from the signature library the classification of the vehicle.
4 Citations
20 Claims
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1. A system for classifying vehicles, the system comprising:
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a non-transitory computer readable memory configured to store a classification library having a plurality of entries, each entry in the library having a vehicle classification; a sequence generating module operably connected to an inductive loop sensor for receiving a vehicle signature therefrom, and configured to convert the vehicle signature into a sequence of numbers; an input set generating module operably connected to the sequence generating module for receiving the sequence, and configured to identify peaks in the sequence and to generate an input set, the input set comprising the sequence, a quantity of peaks in the sequence, a location of a first peak in the sequence, and a value of the first peak; a classification module operably connected to the memory for accessing the classification library, operably connected to the input set generating module for receiving the input set, and configured to perform K-nearest neighbor (KNN) classification with the classification library on the input set thereby determining an estimated vehicle classification for the input set; and an output module configured to output the estimated vehicle classification. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for generating a vehicle classification library, the system comprising:
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a non-transitory computer readable memory storing a plurality of training examples, each training example having a vehicle signature from an inductive loop sensor and a vehicle classification; a grouping module configured to group training examples having a same vehicle classification; a generating module to generate for each group a set of inputs by, for each training example in the group, (i) converting the vehicle signature into a sequence, (ii) identifying peaks in the sequence, and (iii) forming an input containing (A) a quantity of peaks in the sequence, (B) a location of a first peak in the sequence, (C) a value of the first peak, and (D) values of said sequence at select peak locations; a clustering module configured to receive the set of inputs for a group from the generating module and use K-means clustering to organize the inputs of said set into K clusters; a library module configured to create a library entry for each cluster by (i) averaging the value of the location of the first peak among the inputs in said cluster, (ii) averaging the value of the first peak among the inputs in said cluster, (iii) averaging the value of the quantity of peaks among the inputs in said cluster, and (iv) averaging the sequences corresponding to the inputs in said cluster at each position in said sequences; and a recording module to add each library entry created by the library module to the vehicle classification library. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. A method for generating a vehicle classification library, the method comprising:
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receiving a plurality of training examples, each training example having a vehicle signature from an inductive loop sensor and a vehicle classification; grouping the training examples having a same vehicle classification; and for each group generating a set of library inputs by, for each of the training examples in the group, converting the vehicle signature into a sequence; identifying peaks in the sequence; calculating a library input comprising a quantity of peaks in the sequence, a location of a first peak in the sequence, a value of the first peak, and values of said sequence at select peak locations; clustering library inputs in the set of library inputs into K clusters using a K-means clustering; creating a library entry for each cluster by averaging the value of the location of the first peak among the library inputs in said cluster; averaging the value of the first peak among the library inputs in said cluster; averaging the value of the number of peaks among the library inputs in said cluster; and averaging the sequences corresponding to the library inputs in said cluster at each position in the sequences; and adding the library entry to a vehicle classification library. - View Dependent Claims (20)
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Specification