Non-volitional discontinuation of motion, namely bradykinesia, is a common motor symptom among patients with Parkinson's disease (PD). Evaluating bradykinesia severity is an important part of clinical examinations on PD patients in both diagnosis and monitoring phases. However, subjective evaluations from different clinicians often show low consistency. The research works that explore objective quantification of bradykinesia are mostly based on highly-integrated sensors. Although these sensor-based methods demonstrate applaudable performance, it is unrealistic to promote them for wide use because the special devices they require are far from popularized in daily lives. In this paper, we take advantage of computer vision and machine learning technologies, proposing a vision-based method to automatically and objectively quantify bradykinesia severity. Three bradykinesia-related items are investigated in our study: finger tapping, hand clasping and hand pro/supination. In our method, human pose estimation technology is utilized to extract kinematic characteristics and supervised-learning-based classifiers are employed to generate score ratings. Clinical experiment on 60 patients shows that the scoring accuracy of our method over 360 examination videos is 89.7%, which is competitive with other related works. The devices our method requires are only a camera for instrumentation and a laptop for data processing. Therefore, our method can produce reliable assessment results on Parkinsonian bradykinesia with minimal device requirement, showing great potential of realizing long-term remote monitoring on patients' condition.