征二级网站建设意见 通知,黑龙江建设网官网手机版,动态页网站,网站被k多久可以恢复在很多公共场合中#xff0c;因为一些不可控因素导致最终爆发打架斗殴或者大规则冲突事件的案例层出不穷#xff0c;基于视频监控等技术手段智能自动化地识别出已有或者潜在的危险行为对于维护公共场合的安全稳定有着重要的意义。本文的核心目的就是想要基于CNN模型来尝试开发…在很多公共场合中因为一些不可控因素导致最终爆发打架斗殴或者大规则冲突事件的案例层出不穷基于视频监控等技术手段智能自动化地识别出已有或者潜在的危险行为对于维护公共场合的安全稳定有着重要的意义。本文的核心目的就是想要基于CNN模型来尝试开发构建公众场景下的打架斗殴行为识别系统这里我们从互联网中采集了相当批量的聚众数据首先看下效果 接下来看下数据集
【有打架斗殴】 【无打架斗殴】 数据主要来源于互联网数据采集和后期的人工处理标注。
可以借助我编写的数据集随机划分函数便捷地生成训练集-测试集如下所示
def randomSplit(dataDirdata/,train_ratio0.80):数据集随机划分if not os.path.exists(labels.json) or not os.path.exists(dataset.json):pic_list[]labels_list os.listdir(dataDir)labels_list.sort()print(labels_list: , labels_list)with open(labels.json, w) as f:f.write(json.dumps(labels_list))for one_label in os.listdir(dataDir):oneDirdataDirone_label/one_list[oneDirone for one in os.listdir(oneDir)]pic_listone_listlengthlen(pic_list)print(length: , length)train_numint(length*train_ratio)test_numlength-train_numprint(train_num: , train_num, , test_num: , test_num)train_listrandom.sample(pic_list, train_num)test_list[one for one in pic_list if one not in train_list]dataset{}dataset[train]train_listdataset[test]test_listwith open(dataset.json,w) as f:f.write(json.dumps(dataset))else:print(Not Need To SplitData Again!!!!!)
接下来需要加载本地图像数据来解析创建可直接用于后续模型训练计算的数据集核心实现如下所示
def buildDataset():加载本地数据集创建数据集X_train, y_train [], []X_test, y_test [], []train_listdataset[train]test_listdataset[test]picDir data/train/#训练集for one_path in train_list:try:print(one_path: , one_path)one_img parse4Img(one_path)one_y parse4Label(one_pic_classes,labels_list)X_train.append(one_img)y_train.append(one_y)except Exception as e:print(train Exception: , e)X_train np.array(X_train)#测试集for one_path in test_list:try:print(one_path: , one_path)one_img parse4Img(one_path)one_y parse4Label(one_pic_classes,labels_list)X_test.append(one_img)y_test.append(one_y)except Exception as e:print(test Exception: , e)X_test np.array(X_test)
完成数据集的解析构建之后就可以进行模型的开发训练工作了。
这部分可以参考我前面的博文的实现即可这里就不再赘述了。本文中搭建的轻量级模型结构详情如下所示
{class_name: Sequential,config: {name: sequential_1,layers: [{class_name: Conv2D,config: {name: conv2d_1,trainable: true,batch_input_shape: [null, 100, 100, 3],dtype: float32,filters: 64,kernel_size: [3, 3],strides: [2, 2],padding: same,data_format: channels_last,dilation_rate: [1, 1],activation: relu,use_bias: true,kernel_initializer: {class_name: RandomUniform,config: {minval: -0.05,maxval: 0.05,seed: null}},bias_initializer: {class_name: Zeros,config: {}},kernel_regularizer: null,bias_regularizer: null,activity_regularizer: null,kernel_constraint: null,bias_constraint: null}}, {class_name: MaxPooling2D,config: {name: max_pooling2d_1,trainable: true,pool_size: [2, 2],padding: valid,strides: [2, 2],data_format: channels_last}}, {class_name: Conv2D,config: {name: conv2d_2,trainable: true,filters: 128,kernel_size: [3, 3],strides: [2, 2],padding: same,data_format: channels_last,dilation_rate: [1, 1],activation: relu,use_bias: true,kernel_initializer: {class_name: RandomUniform,config: {minval: -0.05,maxval: 0.05,seed: null}},bias_initializer: {class_name: Zeros,config: {}},kernel_regularizer: null,bias_regularizer: null,activity_regularizer: null,kernel_constraint: null,bias_constraint: null}}, {class_name: MaxPooling2D,config: {name: max_pooling2d_2,trainable: true,pool_size: [2, 2],padding: valid,strides: [2, 2],data_format: channels_last}}, {class_name: Conv2D,config: {name: conv2d_3,trainable: true,filters: 256,kernel_size: [3, 3],strides: [2, 2],padding: same,data_format: channels_last,dilation_rate: [1, 1],activation: relu,use_bias: true,kernel_initializer: {class_name: RandomUniform,config: {minval: -0.05,maxval: 0.05,seed: null}},bias_initializer: {class_name: Zeros,config: {}},kernel_regularizer: null,bias_regularizer: null,activity_regularizer: null,kernel_constraint: null,bias_constraint: null}}, {class_name: MaxPooling2D,config: {name: max_pooling2d_3,trainable: true,pool_size: [2, 2],padding: valid,strides: [2, 2],data_format: channels_last}}, {class_name: Flatten,config: {name: flatten_1,trainable: true,data_format: channels_last}}, {class_name: Dense,config: {name: dense_1,trainable: true,units: 256,activation: relu,use_bias: true,kernel_initializer: {class_name: VarianceScaling,config: {scale: 1.0,mode: fan_avg,distribution: uniform,seed: null}},bias_initializer: {class_name: Zeros,config: {}},kernel_regularizer: null,bias_regularizer: null,activity_regularizer: null,kernel_constraint: null,bias_constraint: null}}, {class_name: Dropout,config: {name: dropout_1,trainable: true,rate: 0.1,noise_shape: null,seed: null}}, {class_name: Dense,config: {name: dense_2,trainable: true,units: 512,activation: relu,use_bias: true,kernel_initializer: {class_name: VarianceScaling,config: {scale: 1.0,mode: fan_avg,distribution: uniform,seed: null}},bias_initializer: {class_name: Zeros,config: {}},kernel_regularizer: null,bias_regularizer: null,activity_regularizer: null,kernel_constraint: null,bias_constraint: null}}, {class_name: Dropout,config: {name: dropout_2,trainable: true,rate: 0.15,noise_shape: null,seed: null}}, {class_name: Dense,config: {name: dense_3,trainable: true,units: 2,activation: softmax,use_bias: true,kernel_initializer: {class_name: VarianceScaling,config: {scale: 1.0,mode: fan_avg,distribution: uniform,seed: null}},bias_initializer: {class_name: Zeros,config: {}},kernel_regularizer: null,bias_regularizer: null,activity_regularizer: null,kernel_constraint: null,bias_constraint: null}}]},keras_version: 2.2.4,backend: tensorflow
}
默认200次epoch的迭代计算训练完成后对loss曲线和acc曲线进行了对比可视化展示如下所示
【loss对比曲线】 【acc对比曲线】 可以看到模型的效果还是很不错的。
最后编写专用的可视化系统界面进行实例化推理展示实例结果如下所示 后续的工作考虑结合视频连续帧的特点来进一步提升打架斗殴行为的识别精度感兴趣的话也都可以自行尝试实践一下。
