init

.gitignore

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+*.bmp

readme.md

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+# VGG_CF
+Visual Object Tracking based on Correlation Filter using VGG feature. About 2s per frame, can be faster via optimize the code.  
+Precision rate:86.3%, Success rate: 60.3%
+
+# Dependences
+CUDA8.0  
+cudnn6.0  
+python==3.6  
+GPU ~=4.5G memory
+
+# Tracking
+1. git clone https://github.com/wangchaoqun56/VGG_CF.git
+2. pip install -r requirments.txt
+3. cd tracking  
+	eidt configer.py  
+	data_path: path to dataset  
+	vgg_model_path: path to vgg19 model pretrained by ImageNet
+4. python tracker.py -s 0 -e 100  
+	--start: index of first sequence  
+	--end: index of last sequence  
+	--gpu: gpu id default='0'
+
+# Other
+download vgg19.npy from [vgg19.npy](https://mega.nz/#!xZ8glS6J!MAnE91ND_WyfZ_8mvkuSa2YcA7q-1ehfSm-Q1fxOvvs)

requirements.txt

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+tensorflow-gpu==1.2.1
+scipy==1.2.1
+pillow==6.0.0
+scikit_image==0.15.0

tracking/Logger.py

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+# Logger.py
+
+import logging
+
+class Logger():
+    def __init__(self, logname, loglevel, logger):
+
+        self.logger = logging.getLogger(logger)
+        self.logger.setLevel(logging.DEBUG)
+
+        fh = logging.FileHandler(logname)
+        fh.setLevel(logging.DEBUG)
+
+        ch = logging.StreamHandler()
+        ch.setLevel(logging.DEBUG)
+
+        formatter = logging.Formatter('%(asctime)s-%(name)s-%(levelname)s-%(message)s')
+        #formatter = format_dict[int(loglevel)]
+        fh.setFormatter(formatter)
+        ch.setFormatter(formatter)
+
+        self.logger.addHandler(fh)
+        self.logger.addHandler(ch)
+
+    def getlog(self):
+        return self.logger

tracking/configer.py

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+import os
+import time
+
+import numpy as np
+import tensorflow as tf
+
+import vgg19_tf
+from funs_tracking import *
+from Logger import *
+from vgg_utis import vgg_process_images, vgg_resize_maps
+
+#########################
+##### gpu parameter #####
+#########################
+gpu_id = '/gpu:0'
+config = tf.ConfigProto(allow_soft_placement=True)
+config.gpu_options.allow_growth = True
+
+#########################
+#### data params ########
+#########################
+
+# data_path = r'/data/cyy/Data/CVPR2013Bechmark_Color/'
+# cache_path = r'/data/cyy/Results/vgg_cf_all_scale'
+data_path = r'/data3/TB-100/OTB2015'
+cache_path = r'./Results'
+if not os.path.isdir(cache_path):
+    os.mkdir(cache_path)
+pstr = 'gcnn'
+
+####
+padding = {'large': 1, 'height': 0.4, 'generic': 2}  # 25~50: 2.5 others 2.2
+cell_size0 = 4
+batch_size = 1  # fixed
+max_win2 = 1600
+min_win2 = 1600
+fea_sz = np.asarray([57, 57])
+#########################
+####### VGG Model #######
+#########################
+
+vgg_model_path = '/data2/wangchaoqun/model/vgg19.npy'
+vgg_batch_size = 1
+vgg_out_layers = np.asarray((10, 11, 12, 14, 15, 16))
+
+
+vgg_is_lrn = False
+
+# image processing params for vgg
+img_param = {}
+img_param['interp_tool'] = 'misc'  # misc or skimage
+img_param['interp'] = 'bilinear'
+img_param['normal_hw'] = (224, 224)
+img_param['normal_type'] = 'keep_all_content'
+
+##################################
+###### graph parameters ##########
+##################################
+
+gh1 = {'height_width': None, 'number_edges': 4, 'metric': 'euclidean', 'normalized_laplacian': True}
+
+# pca params
+pca_is_mean = True
+pca_is_norm = False
+pca_energy = 100
+####
+nn_p = 6  #
+nn_K = 20
+nn_gamma = 1.0
+
+####################### cf params ###############################
+search_scale = fun_get_search_scale()
+
+kernel_sigma = 0.5
+kernel_type = 'linear'
+kernel_gamma = 1  # 1.e-6
+update_factor = 0.0075  # Jogging learning 0.005, others 0.015
+cf_nframe_update = 1
+weight_update_factor = 0.01