Home/Seminar/VASC Seminar/Comparing apples and oranges: Off-road pedestrian detection on the NREC agricultural person-detection dataset
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VASC Seminar


Zach Pezzementi Lead Robotics Engineer Carnegie Mellon University / NREC
December 12, 2017
3:00 pm
- 4:00 pm
GHC 6501
Comparing apples and oranges: Off-road pedestrian detection on the NREC agricultural person-detection dataset

Abstract: Person detection from vehicles has made rapid progress recently with the advent of multiple high-quality datasets of urban and highway driving, yet no large-scale benchmark has been available for the same problem in off-road or agricultural environments. In this talk, we present the NREC Agricultural Person-Detection Dataset to spur research in these environments. It consists of labeled stereo video of people in orange and apple orchards taken from two perception platforms (a tractor and a pickup truck), along with vehicle position data from RTK GPS. We define a benchmark on part of the dataset that combines a total of 76k labeled person images and 19k sampled person-free images. The dataset highlights several key challenges of the domain, including varying environment, substantial occlusion by vegetation, people in motion and in non-standard poses, and people seen from a variety of distances; meta-data are included to allow targeted evaluation of each of these effects.


We’ll also go over analysis of the dataset’s intended usage: We present baseline detection performance results for three leading approaches from urban pedestrian detection and our own convolutional neural network approach that benefits from the incorporation of additional image context. We show that the success of existing approaches on urban data does not transfer directly to this domain. Finally, we’ll show some other things that the rich data allow you to do.


The dataset and associated benchmark results are available here: https://www.nrec.ri.cmu.edu/nrec/solutions/agriculture/human-detection-and-tracking.html


Bio: Zachary Pezzementi is a senior robotics engineer at the National Robotics Engineering Center at Carnegie-Mellon University. His research interests focus on robotic perception and machine learning, with applications in robotics.

Zach received his Bachelor’s degrees in Computer Science and Engineering from Swarthmore College in 2005. He received his Masters and Ph.D. in Computer Science from Johns Hopkins University, where he worked on haptics and visual tracking for surgical robotics. He defended his thesis on “Object Recognition with Tactile Force Sensing” in 2011 and then joined NREC, where he has worked on projects in mobile robotics, agricultural automation, and robotic safeguarding.


Homepage: http://www.cs.jhu.edu/~zap/research.html