Existing smoke vehicle detection methods are vulnerable to false alarms. To solve this issue, this paper presents two automatic smoke vehicle detection methods based on spatiotemporal bag-of-features (S-BoF) and professional convolutional neural network (P-CNN). In the first method, we propose the S-BoF model to characterize the key regions detected by the visual background extractor (ViBe) algorithm. The S-BoF model contains three groups of features, including color moments on three orthogonal planes (CM-TOP), completed robust local binary pattern on three orthogonal planes (CRLBP-TOP), and histogram of oriented gradient on three orthogonal planes (HOG-TOP). The extracted features are fed to the support vector machine (SVM) and classify the key regions to smoke regions or non-smoke regions to further detect smoke vehicles. In the second method, we propose the P-CNN model to extract more robust and complementary spatiotemporal features by designing three professional models to analyze different kinds of features in the key region sequence on three orthogonal planes. The three professional models, including color CNN (CCNN), texture CNN (TCNN), and gradient CNN (GCNN), are based on three independent CNN128 models with different inputs. The experimental results show that the proposed methods achieve higher detection rates and lower false alarm rates than existing smoke detection methods.