Information
Contact Name: Jong Taek Lee
Email: jtlee@utexas.edu
Organization: Electronics and Telecommunications Research Institute (ETRI)
Method's Name: (C) Ensemble of Local Expert and Global Networks
Reference: Jong Taek Lee, et al., "Deep Learning-based Vehicle Classification using an Ensemble of Local Expert and Global Networks", Traffic Surveillance Workshop and Challenge, CVPR 2017.
Project Website:
Evaluation Metrics

Precision of each category :

$$ Pre_i = \frac{TP_i}{TP_i + FP_i}$$


Articulated Truck Bicycle Bus Car Motorcycle Non-motorized Vehicle Pedestrian Pickup Truck Single Unit Truck Work Van Background
0.9457 0.9142 0.9892 0.9822 0.9212 0.8224 0.9666 0.9461 0.8238 0.9183 0.9981

Recall of each category:

$$ Rec_i = \frac{TP_i}{TP_i + FN_i}$$


Articulated Truck Bicycle Bus Car Motorcycle Non-motorized Vehicle Pedestrian Pickup Truck Single Unit Truck Work Van Background
0.9358 0.8774 0.9620 0.9889 0.9212 0.6872 0.9425 0.9507 0.8289 0.8353 0.9966

Accuracy: 0.9792

$$ Acc = \frac{TP}{Number of Testing Images}$$


Mean Recall: 0.9024

$$ {mRec} = {\rm mean}({Rec_i})$$


Mean Precision: 0.9298

$$ {mPre} = {\rm mean}({Pre_i})$$


Cohen Kappa Score: 0.9675

Confusion Matrix (%):

Predicted
Articulated Truck Bicycle Bus Car Motorcycle Non-motorized Vehicle Pedestrian Pickup Truck Single Unit Truck Work Van Background
True Articulated Truck 93.58 0.00 0.19 0.50 0.00 0.62 0.00 0.15 4.72 0.08 0.15
Bicycle 0.00 87.74 0.18 0.70 4.20 0.18 6.65 0.00 0.00 0.00 0.35
Bus 0.35 0.00 96.20 0.43 0.00 0.58 0.00 0.12 0.58 1.59 0.16
Car 0.01 0.00 0.00 98.89 0.00 0.01 0.00 0.88 0.01 0.15 0.05
Motorcycle 0.00 0.61 0.00 5.66 92.12 0.40 0.40 0.81 0.00 0.00 0.00
Non-motorized Vehicle 4.79 0.23 0.23 9.13 0.46 68.72 0.46 2.05 10.27 1.60 2.05
Pedestrian 0.00 2.68 0.00 1.02 0.83 0.13 94.25 0.00 0.00 0.00 1.09
Pickup Truck 0.00 0.00 0.03 4.56 0.00 0.06 0.00 95.07 0.18 0.08 0.02
Single Unit Truck 7.73 0.00 0.23 2.11 0.00 0.63 0.00 4.53 82.89 1.56 0.31
Work Van 0.00 0.00 0.21 14.00 0.00 0.04 0.00 1.57 0.66 83.53 0.00
Background 0.01 0.00 0.01 0.28 0.00 0.02 0.02 0.00 0.00 0.00 99.66