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Detection and Classification of Urban Actors Through TensorFlow with an Android Device

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Book cover Information and Communication Technologies of Ecuador (TIC.EC) (TICEC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1099))

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Abstract

With the advancement of Artificial Intelligence, now it is possible to perform Neural Networks for detection and classification inside a device with limited hardware. We compare the performance of Deep Neural Networks (DNN). Model selected is Single Shot Detector (SSD) by default and re-trained, and frameworks are TensorFlow mobile versus TensorFlow lite. Default model contains 80 different classes of objects and the second one is our re-trained model with only 6 different classes based on urban actors (car, bus, truck, bicycle, motorcycle, person). The main goal is to build an object tracker for urban mobility with an Android device. Results are based in two metrics for object detection: mean Average Precision (mAP) and log-average miss rate. And for classification we report two metrics: precision (PRE) and recall (REC). We report inference time as an additional metric which is strongly related to hardware used. TensorFlow mobile is much slower than TensorFlow lite in terms of time of inference. Finally, re-trained model allows the integration of new scenarios, improving the detection rate.

Thanks to the Ecuadorian Corporation for the Development of Research and Academia, CEDIA, for the financing provided to research, through the CEPRA projects, especially the CEPRA project - XII -2018; Clasificador-video para actores de la movilidad como alternativa a conteos volumetricos manuales.

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Author information

Authors and Affiliations

  1. LIDI, Universidad del Azuay, 010204, Cuenca, Ecuador

    Andres Campoverde & Gabriel Barros

  2. tivo.ec Research Institute. Don Bosco 2-07, 010205, Cuenca, Ecuador

    Gabriel Barros

Authors
  1. Andres Campoverde
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  2. Gabriel Barros
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Corresponding author

Correspondence to Andres Campoverde .

Editor information

Editors and Affiliations

  1. ESPE, 171-5-23, Universidad de las Fuerzas Armadas, Sangolqui, Ecuador

    Efrain Fosenca C

  2. Dept de Ciencias de la Compu y Elect, Universidad Técnica Particular, Loja, Ecuador

    Germania Rodríguez Morales

  3. Escuela de Ciencias de la Computación, Universidad del Azuay, Cuenca, Ecuador

    Marcos Orellana Cordero

  4. Mz 55, Villa 4, University of Guayaquil, Guayaquil, Ecuador

    Miguel Botto-Tobar

  5. Universidad del Azuay, Cuenca, Ecuador

    Esteban Crespo Martínez

  6. Universidad del Azuay, Cuenca, Ecuador

    Andrés Patiño León

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Campoverde, A., Barros, G. (2020). Detection and Classification of Urban Actors Through TensorFlow with an Android Device. In: Fosenca C, E., Rodríguez Morales, G., Orellana Cordero, M., Botto-Tobar, M., Crespo Martínez, E., Patiño León, A. (eds) Information and Communication Technologies of Ecuador (TIC.EC). TICEC 2019. Advances in Intelligent Systems and Computing, vol 1099. Springer, Cham. https://doi.org/10.1007/978-3-030-35740-5_12

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