To　improve　the　adaptability　of　existing　vehicle　detection　algorithms　in　complex　traffic　circumstances,　a　robust　detection　algorithm　based　on　co-training　from　semi-supervised　learning　methods　was　proposed.　First,　according　to　a　small　number　of　humanly　labeled　samples,　two　classifiers　were　trained,　which　were　AdaBoost　based　on　Haar-like　features　and　the　SVM　(support　vector　machines)　based　on　HOG　(histograms　of　oriented　gradients)　features,　respectively,　so　that　both　of　them　had　some　identification　ability.　Second,　on　the　basis　of　co-training　from　semi-supervised　learning　framework,　the　new　samples　gained　from　the　two　algorithms　above　were　added　to　mutual　sample　sets　to　increase　the　number　of　training　samples,　and　the　train　was　repeated.　Due　to　the　redundancy　these　two　features　had,　the　detected　positive　and　negative　samples　would　contain　the　images　which　were　missed　out　or　falsely　detected　mutually.　Because　of　the　increasing　number　of　samples,　the　robustness　of　the　new　re-training　classifiers　has　been　greatly　improved　so　that　the　classifiers　can　detect　the　vehicles　accurately.　Besides,　there　will　be　no　need　to　mark　artificially,　but　to　classify　and　mark　the　unlabeled　samples　by　the　algorithms.　Therefore,　it　can　highly　improve　the　adaptability　of　vehicle　detection　algorithm.