I want to test deepstream-3d-action-recognition with 3d model, But I can not find fall_ floor

Accelerated Computing Intelligent Video Analytics DeepStream SDK
1

Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) Jetson AGX
• DeepStream Version 6.3
**• JetPack Jetpack 5.1.1 [L4T 35.3.1]
**• TensorRT Version
**• NVIDIA GPU Driver Version (valid for GPU only) 540.3.0
**• Issue Type( questions, new requirements, bugs) questions
• How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing)

I want to test deepstream-3d-action-recognition with 3d model, But I can not find fall_ floor.

This is my code and log .
I don’t have any screen

I don’t know what the source of this problem.
deepstream-3d-action-recognition.zip (399.3 KB)
log.txt (213.5 KB)

2

The model I download from Action Recognition Net | NVIDIA NGC
The version is deployable_v1.0

1、make the deepstream-3d-action-recognition exe
2、run the app(sink use fakesike)

4

duplicate with 299559 and 301792

1.resnet18_3d_rgb_hmdb5_32.etlt is a classification model, So there will be no bbox and no coordinates.

2.If you want to get the frame number, use the following patch

diff --git a/sources/apps/sample_apps/deepstream-3d-action-recognition/deepstream_3d_action_recognition.cpp b/sources/apps/sample_apps/deepstream-3d-action-recognition/deepstream_3d_action_recognition.cpp
index 9ef1c1e..03bd523 100644
--- a/sources/apps/sample_apps/deepstream-3d-action-recognition/deepstream_3d_action_recognition.cpp
+++ b/sources/apps/sample_apps/deepstream-3d-action-recognition/deepstream_3d_action_recognition.cpp
@@ -91,9 +91,9 @@ add_fps_display_meta(NvDsFrameMeta *frame, NvDsBatchMeta *batch_meta) {
   nvds_add_display_meta_to_frame(frame, display_meta);
 }
 
+static int frame = 0;
 /* tiler_sink_pad_buffer_probe  will extract metadata received on OSD sink pad
  * and update params for drawing rectangle, object information etc. */
-
 static GstPadProbeReturn
 pgie_src_pad_buffer_probe(GstPad *pad, GstPadProbeInfo *info,
                           gpointer u_data)
@@ -103,6 +103,7 @@ pgie_src_pad_buffer_probe(GstPad *pad, GstPadProbeInfo *info,
 
   NvDsMetaList *l_user_meta = NULL;
   NvDsUserMeta *user_meta = NULL;
+  frame++;
   for (l_user_meta = batch_meta->batch_user_meta_list; l_user_meta != NULL;
        l_user_meta = l_user_meta->next)
   {
@@ -121,6 +122,7 @@ pgie_src_pad_buffer_probe(GstPad *pad, GstPadProbeInfo *info,
       }
       for (auto &roi_meta : preprocess_batchmeta->roi_vector)
       {
+        // printf("%.2f %.2f %.2f %.2f \n", roi_meta.roi.left, roi_meta.roi.top, roi_meta.roi.width, roi_meta.roi.height);
         NvDsMetaList *l_user = NULL;
         for (l_user = roi_meta.roi_user_meta_list; l_user != NULL;
              l_user = l_user->next)
@@ -143,6 +145,9 @@ pgie_src_pad_buffer_probe(GstPad *pad, GstPadProbeInfo *info,
             const gchar *label = "";
             if (class_id < MAX_CLASS_LEN)
               label = kActioClasseLabels[class_id];
+            if (!strncasecmp(label, "fall_floor", strlen("fall_floor"))) {
+              printf("fall_floor %d \n", frame);
+            }
             LOG_DEBUG("output tensor result: cls_id: %d, scrore:%.3f, label: %s", class_id, max_prob, label);
           }
         }

Using the test video from this link, you can get the following results.

fall_floor 96 
fall_floor 97 
fall_floor 100 
fall_floor 101 
fall_floor 102 
fall_floor 103 
fall_floor 104 
fall_floor 105 
fall_floor 106 
fall_floor 107 
fall_floor 108

3.I use Jetpack 6.0 and Deepstream 7.0 on AGX Orin. For better performance, you can try setting AGX Orin to maxn model

5

There is no update from you for a period, assuming this is not an issue anymore. Hence we are closing this topic. If need further support, please open a new one. Thanks

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