diff --git a/tracker/multitracker.py b/tracker/multitracker.py
index 4b6168d..eae4683 100644
--- a/tracker/multitracker.py
+++ b/tracker/multitracker.py
@@ -2,6 +2,7 @@ from numba import jit
 from collections import deque
 import torch
 from utils.kalman_filter import KalmanFilter
+from utils.log import logger
 from models import *
 from tracker import matching
 from .basetrack import BaseTrack, TrackState
@@ -42,14 +43,15 @@ class STrack(BaseTrack):
         self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
         
     @staticmethod
-    def multi_predict(stracks):
+    def multi_predict(stracks, kalman_filter):
         if len(stracks) > 0:
             multi_mean = np.asarray([st.mean.copy() for st in stracks])
             multi_covariance = np.asarray([st.covariance for st in stracks])
             for i, st in enumerate(stracks):
                 if st.state != TrackState.Tracked:
                     multi_mean[i][7] = 0
-            multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance)
+#            multi_mean, multi_covariance = STrack.kalman_filter.multi_predict(multi_mean, multi_covariance)
+            multi_mean, multi_covariance = kalman_filter.multi_predict(multi_mean, multi_covariance)
             for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
                 stracks[i].mean = mean
                 stracks[i].covariance = cov
@@ -158,7 +160,7 @@ class STrack(BaseTrack):
 class JDETracker(object):
     def __init__(self, opt, frame_rate=30):
         self.opt = opt
-        self.model = Darknet(opt.cfg, opt.img_size, nID=30)
+        self.model = Darknet(opt.cfg, nID=14455)
         # load_darknet_weights(self.model, opt.weights)
         self.model.load_state_dict(torch.load(opt.weights, map_location='cpu')['model'], strict=False)
         self.model.cuda().eval()
@@ -240,7 +242,7 @@ class JDETracker(object):
         # Combining currently tracked_stracks and lost_stracks
         strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
         # Predict the current location with KF
-        STrack.multi_predict(strack_pool)
+        STrack.multi_predict(strack_pool, self.kalman_filter)
 
 
         dists = matching.embedding_distance(strack_pool, detections)
diff --git a/utils/datasets.py b/utils/datasets.py
index 736b8db..88546a3 100644
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -78,6 +78,9 @@ class LoadImages:  # for inference
 
 class LoadVideo:  # for inference
     def __init__(self, path, img_size=(1088, 608)):
+        if not os.path.isfile(path):
+            raise FileExistsError
+        
         self.cap = cv2.VideoCapture(path)        
         self.frame_rate = int(round(self.cap.get(cv2.CAP_PROP_FPS)))
         self.vw = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))