基于二进制粒子群优化算法的行人检测

Binary particle swarm optimization algorithm for human pedestrian recognition

针对汽车前方道路上的行人安全问题,对道路行人采用二进制粒子群优化算法(BPSO)进行了检测,以确保行人的安全。首先,对随机采集的道路行人图像样本进行了二维离散余弦变换(DCT),将行人的描述从图像空间转换为用少量数据点来表示频率域空间,再利用DCT算法的对称性,解压缩图像,获得了行人图像的特征向量;其次,应用BPSO算法对得到的特征向量进行了特征选择,从行人频域特征空间中,提取了有价值的特征子集,得到了最具代表性的行人特征,完成了行人检测。试验结果表明,在样本数量较少的情况下,无论在检测正确率还是检测实时性方面BPSO算法都优于传统的支持向量机(SVM)算法。研究结果表明,二进制粒子群优化算法能够高效快速的检测到行人,为车辆主动安全技术提供重要基础,对于减少交通事故具有重要意义。

Aiming at cars on the road in front of pedestrian safety issues, binary particle swarm optimization（BPSO） was used to detect, in order to ensure the safety of pedestrians. Firstly, the collected pedestrian sample images on the road changed into feature vectors by two-di- mensional discrete cosine transform（DCT）. The description of the pedestrian was converted to a small number of data points from the image space to the frequency domain space. Use the symmetry of the DCT algorithm, decompress image, get pedestrian image feature vector. Second- ly, the features were selected by the BPSO algorithm from the feature vectors, from the the space pedestrian frequency domain, extracting valu- able feature subset,in order to get the most representative features of pedestrian, completing the pedestrian detection. The results show that BPSO algorithm in the case of the small sample size, both in real-time detect the correct rate or detection are superior to traditional support vector machine （SVM） algorithm. The results indicate that this research can be efficient and fast detect pedestrians, provide important basis for vehicle active safety technologies, to reduce the number of traffic accidents, that is of great significance.