YoloV3 Deepstream SDK 4.0 performance on Jetson Nano

I have test YoloV3 example of DS 4.0 with some issue.

1.I’m running with default config file deepstream_app_config_yoloV3.txt. The overall frame rate is about 1~2 fps. Is it correct ?

2.It takes several minutes to do “Building the TensorRT Engine…”. Is there way to do it ONLY for the very first time running ?

Below is the procdure and result

$ bzip2 -dc deepstream_sdk_v4.0_jetson.tbz2 | tar xvf -

$ cd deepstream_sdk_v4.0_jetson

$ sudo apt-get install \
    libssl1.0.0 \
    libgstreamer1.0-0 \
    gstreamer1.0-tools \
    gstreamer1.0-plugins-good \
    gstreamer1.0-plugins-bad \
    gstreamer1.0-plugins-ugly \
    gstreamer1.0-libav \
    libgstrtspserver-1.0-0 \
    libjansson4

$ sudo tar -xvf binaries.tbz2 -C /

$ sudo ./install.sh

$ sudo ldconfig

$ rm ${HOME}/.cache/gstreamer-1.0/registry.aarch64.bin

$ cd sources/objectDetector_Yolo

$ ./prebuild.sh

$ cd nvdsinfer_custom_impl_Yolo

$ export CUDA_VER=10.0

$ make

g++ -c -o nvdsinfer_yolo_engine.o -Wall -std=c++11 -shared -fPIC -I../../includes -I/usr/local/cuda-10.0/include nvdsinfer_yolo_engine.cpp
g++ -c -o nvdsparsebbox_Yolo.o -Wall -std=c++11 -shared -fPIC -I../../includes -I/usr/local/cuda-10.0/include nvdsparsebbox_Yolo.cpp
g++ -c -o yoloPlugins.o -Wall -std=c++11 -shared -fPIC -I../../includes -I/usr/local/cuda-10.0/include yoloPlugins.cpp
g++ -c -o trt_utils.o -Wall -std=c++11 -shared -fPIC -I../../includes -I/usr/local/cuda-10.0/include trt_utils.cpp
g++ -c -o yolo.o -Wall -std=c++11 -shared -fPIC -I../../includes -I/usr/local/cuda-10.0/include yolo.cpp
/usr/local/cuda-10.0/bin/nvcc -c -o kernels.o --compiler-options '-fPIC' kernels.cu
g++ -o libnvdsinfer_custom_impl_Yolo.so  nvdsinfer_yolo_engine.o nvdsparsebbox_Yolo.o yoloPlugins.o trt_utils.o yolo.o kernels.o -shared -Wl,--start-group -lnvinfer_plugin -lnvinfer -lnvparsers -L/usr/local/cuda-10.0/lib64 -lcudart -lcublas -lstdc++fs -Wl,--end-group

$ cd ..

$ deepstream-app -c deepstream_app_config_yoloV3.txt

Using winsys: x11 
Creating LL OSD context new
0:00:00.777696804  8123     0x36493010 INFO                 nvinfer gstnvinfer.cpp:519:gst_nvinfer_logger:<primary_gie_classifier> NvDsInferContext[UID 1]:initialize(): Trying to create engine from model files
0:00:01.032222953  8123     0x36493010 WARN                 nvinfer gstnvinfer.cpp:515:gst_nvinfer_logger:<primary_gie_classifier> NvDsInferContext[UID 1]:generateTRTModel(): INT8 not supported by platform. Trying FP16 mode.
Loading pre-trained weights...
Loading complete!
Total Number of weights read : 62001757
      layer               inp_size            out_size       weightPtr
(1)   conv-bn-leaky     3 x 608 x 608      32 x 608 x 608    992   
(2)   conv-bn-leaky    32 x 608 x 608      64 x 304 x 304    19680 
(3)   conv-bn-leaky    64 x 304 x 304      32 x 304 x 304    21856 
(4)   conv-bn-leaky    32 x 304 x 304      64 x 304 x 304    40544 
(5)   skip             64 x 304 x 304      64 x 304 x 304        - 
(6)   conv-bn-leaky    64 x 304 x 304     128 x 152 x 152    114784
(7)   conv-bn-leaky   128 x 152 x 152      64 x 152 x 152    123232
(8)   conv-bn-leaky    64 x 152 x 152     128 x 152 x 152    197472
(9)   skip            128 x 152 x 152     128 x 152 x 152        - 
(10)  conv-bn-leaky   128 x 152 x 152      64 x 152 x 152    205920
(11)  conv-bn-leaky    64 x 152 x 152     128 x 152 x 152    280160
(12)  skip            128 x 152 x 152     128 x 152 x 152        - 
(13)  conv-bn-leaky   128 x 152 x 152     256 x  76 x  76    576096
(14)  conv-bn-leaky   256 x  76 x  76     128 x  76 x  76    609376
(15)  conv-bn-leaky   128 x  76 x  76     256 x  76 x  76    905312
(16)  skip            256 x  76 x  76     256 x  76 x  76        - 
(17)  conv-bn-leaky   256 x  76 x  76     128 x  76 x  76    938592
(18)  conv-bn-leaky   128 x  76 x  76     256 x  76 x  76    1234528
(19)  skip            256 x  76 x  76     256 x  76 x  76        - 
(20)  conv-bn-leaky   256 x  76 x  76     128 x  76 x  76    1267808
(21)  conv-bn-leaky   128 x  76 x  76     256 x  76 x  76    1563744
(22)  skip            256 x  76 x  76     256 x  76 x  76        - 
(23)  conv-bn-leaky   256 x  76 x  76     128 x  76 x  76    1597024
(24)  conv-bn-leaky   128 x  76 x  76     256 x  76 x  76    1892960
(25)  skip            256 x  76 x  76     256 x  76 x  76        - 
(26)  conv-bn-leaky   256 x  76 x  76     128 x  76 x  76    1926240
(27)  conv-bn-leaky   128 x  76 x  76     256 x  76 x  76    2222176
(28)  skip            256 x  76 x  76     256 x  76 x  76        - 
(29)  conv-bn-leaky   256 x  76 x  76     128 x  76 x  76    2255456
(30)  conv-bn-leaky   128 x  76 x  76     256 x  76 x  76    2551392
(31)  skip            256 x  76 x  76     256 x  76 x  76        - 
(32)  conv-bn-leaky   256 x  76 x  76     128 x  76 x  76    2584672
(33)  conv-bn-leaky   128 x  76 x  76     256 x  76 x  76    2880608
(34)  skip            256 x  76 x  76     256 x  76 x  76        - 
(35)  conv-bn-leaky   256 x  76 x  76     128 x  76 x  76    2913888
(36)  conv-bn-leaky   128 x  76 x  76     256 x  76 x  76    3209824
(37)  skip            256 x  76 x  76     256 x  76 x  76        - 
(38)  conv-bn-leaky   256 x  76 x  76     512 x  38 x  38    4391520
(39)  conv-bn-leaky   512 x  38 x  38     256 x  38 x  38    4523616
(40)  conv-bn-leaky   256 x  38 x  38     512 x  38 x  38    5705312
(41)  skip            512 x  38 x  38     512 x  38 x  38        - 
(42)  conv-bn-leaky   512 x  38 x  38     256 x  38 x  38    5837408
(43)  conv-bn-leaky   256 x  38 x  38     512 x  38 x  38    7019104
(44)  skip            512 x  38 x  38     512 x  38 x  38        - 
(45)  conv-bn-leaky   512 x  38 x  38     256 x  38 x  38    7151200
(46)  conv-bn-leaky   256 x  38 x  38     512 x  38 x  38    8332896
(47)  skip            512 x  38 x  38     512 x  38 x  38        - 
(48)  conv-bn-leaky   512 x  38 x  38     256 x  38 x  38    8464992
(49)  conv-bn-leaky   256 x  38 x  38     512 x  38 x  38    9646688
(50)  skip            512 x  38 x  38     512 x  38 x  38        - 
(51)  conv-bn-leaky   512 x  38 x  38     256 x  38 x  38    9778784
(52)  conv-bn-leaky   256 x  38 x  38     512 x  38 x  38    10960480
(53)  skip            512 x  38 x  38     512 x  38 x  38        - 
(54)  conv-bn-leaky   512 x  38 x  38     256 x  38 x  38    11092576
(55)  conv-bn-leaky   256 x  38 x  38     512 x  38 x  38    12274272
(56)  skip            512 x  38 x  38     512 x  38 x  38        - 
(57)  conv-bn-leaky   512 x  38 x  38     256 x  38 x  38    12406368
(58)  conv-bn-leaky   256 x  38 x  38     512 x  38 x  38    13588064
(59)  skip            512 x  38 x  38     512 x  38 x  38        - 
(60)  conv-bn-leaky   512 x  38 x  38     256 x  38 x  38    13720160
(61)  conv-bn-leaky   256 x  38 x  38     512 x  38 x  38    14901856
(62)  skip            512 x  38 x  38     512 x  38 x  38        - 
(63)  conv-bn-leaky   512 x  38 x  38    1024 x  19 x  19    19624544
(64)  conv-bn-leaky  1024 x  19 x  19     512 x  19 x  19    20150880
(65)  conv-bn-leaky   512 x  19 x  19    1024 x  19 x  19    24873568
(66)  skip           1024 x  19 x  19    1024 x  19 x  19        - 
(67)  conv-bn-leaky  1024 x  19 x  19     512 x  19 x  19    25399904
(68)  conv-bn-leaky   512 x  19 x  19    1024 x  19 x  19    30122592
(69)  skip           1024 x  19 x  19    1024 x  19 x  19        - 
(70)  conv-bn-leaky  1024 x  19 x  19     512 x  19 x  19    30648928
(71)  conv-bn-leaky   512 x  19 x  19    1024 x  19 x  19    35371616
(72)  skip           1024 x  19 x  19    1024 x  19 x  19        - 
(73)  conv-bn-leaky  1024 x  19 x  19     512 x  19 x  19    35897952
(74)  conv-bn-leaky   512 x  19 x  19    1024 x  19 x  19    40620640
(75)  skip           1024 x  19 x  19    1024 x  19 x  19        - 
(76)  conv-bn-leaky  1024 x  19 x  19     512 x  19 x  19    41146976
(77)  conv-bn-leaky   512 x  19 x  19    1024 x  19 x  19    45869664
(78)  conv-bn-leaky  1024 x  19 x  19     512 x  19 x  19    46396000
(79)  conv-bn-leaky   512 x  19 x  19    1024 x  19 x  19    51118688
(80)  conv-bn-leaky  1024 x  19 x  19     512 x  19 x  19    51645024
(81)  conv-bn-leaky   512 x  19 x  19    1024 x  19 x  19    56367712
(82)  conv-linear    1024 x  19 x  19     255 x  19 x  19    56629087
(83)  yolo            255 x  19 x  19     255 x  19 x  19    56629087
(84)  route                  -            512 x  19 x  19    56629087
(85)  conv-bn-leaky   512 x  19 x  19     256 x  19 x  19    56761183
(86)  upsample        256 x  19 x  19     256 x  38 x  38        - 
(87)  route                  -            768 x  38 x  38    56761183
(88)  conv-bn-leaky   768 x  38 x  38     256 x  38 x  38    56958815
(89)  conv-bn-leaky   256 x  38 x  38     512 x  38 x  38    58140511
(90)  conv-bn-leaky   512 x  38 x  38     256 x  38 x  38    58272607
(91)  conv-bn-leaky   256 x  38 x  38     512 x  38 x  38    59454303
(92)  conv-bn-leaky   512 x  38 x  38     256 x  38 x  38    59586399
(93)  conv-bn-leaky   256 x  38 x  38     512 x  38 x  38    60768095
(94)  conv-linear     512 x  38 x  38     255 x  38 x  38    60898910
(95)  yolo            255 x  38 x  38     255 x  38 x  38    60898910
(96)  route                  -            256 x  38 x  38    60898910
(97)  conv-bn-leaky   256 x  38 x  38     128 x  38 x  38    60932190
(98)  upsample        128 x  38 x  38     128 x  76 x  76        - 
(99)  route                  -            384 x  76 x  76    60932190
(100) conv-bn-leaky   384 x  76 x  76     128 x  76 x  76    60981854
(101) conv-bn-leaky   128 x  76 x  76     256 x  76 x  76    61277790
(102) conv-bn-leaky   256 x  76 x  76     128 x  76 x  76    61311070
(103) conv-bn-leaky   128 x  76 x  76     256 x  76 x  76    61607006
(104) conv-bn-leaky   256 x  76 x  76     128 x  76 x  76    61640286
(105) conv-bn-leaky   128 x  76 x  76     256 x  76 x  76    61936222
(106) conv-linear     256 x  76 x  76     255 x  76 x  76    62001757
(107) yolo            255 x  76 x  76     255 x  76 x  76    62001757
Output blob names :
yolo_83
yolo_95
yolo_107
Total number of layers: 257
Total number of layers on DLA: 0
Building the TensorRT Engine...


Building complete!
0:18:18.114447752  8123     0x36493010 INFO                 nvinfer gstnvinfer.cpp:519:gst_nvinfer_logger:<primary_gie_classifier> NvDsInferContext[UID 1]:generateTRTModel(): Storing the serialized cuda engine to file at /home/gigijoe/deepstream_sdk_v4.0_jetson/sources/objectDetector_Yolo/model_b1_fp16.engine
Deserialize yoloLayerV3 plugin: yolo_83
Deserialize yoloLayerV3 plugin: yolo_95
Deserialize yoloLayerV3 plugin: yolo_107

Runtime commands:
	h: Print this help
	q: Quit

	p: Pause
	r: Resume

NOTE: To expand a source in the 2D tiled display and view object details, left-click on the source.
      To go back to the tiled display, right-click anywhere on the window.


**PERF: FPS 0 (Avg)	
**PERF: 0.00 (0.00)	
** INFO: <bus_callback:163>: Pipeline ready


**PERF: 0.00 (0.00)	
**PERF: 0.00 (0.00)	
Opening in BLOCKING MODE 
NvMMLiteOpen : Block : BlockType = 261 
NVMEDIA: Reading vendor.tegra.display-size : status: 6 
NvMMLiteBlockCreate : Block : BlockType = 261 
** INFO: <bus_callback:149>: Pipeline running

Creating LL OSD context new
**PERF: 1.90 (1.90)	
**PERF: 1.90 (1.90)	
**PERF: 1.91 (1.90)	
...
...
...
**PERF: 1.89 (1.91)	
**PERF: 1.90 (1.91)	
** INFO: <bus_callback:186>: Received EOS. Exiting ...

Quitting
App run successful

Hi stevegigijoe,

I am also getting around the same fps. I believe this sample does not make use of tracker. It would be great if we can figure out how to use tracker with this sample so that the performance significantly improves.

If anyone has achieved this, kindly help us out.

Hi,

1. To stop rebuilding the TensorRT engine, please update the TensorRT cache path:

In config_infer_primary_yoloV3.txt

model-engine-file=[cache file name]

2. Please help to monitor the system status with tegrastats.
The GPU utilization should reach 99% and please let us know if it doesn’t.

sudo tegrastats

Thanks.

Hello, I’m currently experiencing the same problem with reference YoloV3 app.

  • FPS 1.91

  • GR3D_FREQ 99%921

  • Jetson Nano

  • DeepStream 4.0

  • TensorRT 5.1.6.1

  • CUDA 10.0

Do you have any tips?
Thanks

UPDATE

After some testing and reading docs and DeepStreamSDK’s page I think the YoloV3 example app is not supposed/optimized for running on Nano platform. The YoloV3_tiny however really is. On Tiny version I have constant ~25fps for 1080p stream.

Hi AastaLLL,

Below is my tegrastats while running yolov3,

RAM 2157/3963MB (lfb 70x4MB) SWAP 140/8126MB (cached 3MB) IRAM 0/252kB(lfb 252kB) CPU [2%@1428,4%@1428,8%@1428,100%@1428] EMC_FREQ 2%@1600 GR3D_FREQ 0%@921 APE 25 PLL@40.5C CPU@45.5C PMIC@100C GPU@43.5C AO@52.5C thermal@45.25C POM_5V_IN 3244/5475 POM_5V_GPU 173/2186 POM_5V_CPU 1252/653
RAM 2207/3963MB (lfb 70x4MB) SWAP 140/8126MB (cached 3MB) IRAM 0/252kB(lfb 252kB) CPU [3%@1428,4%@1428,8%@1428,100%@1428] EMC_FREQ 3%@1600 GR3D_FREQ 3%@921 APE 25 PLL@40.5C CPU@45.5C PMIC@100C GPU@43.5C AO@52.5C thermal@44.5C POM_5V_IN 3499/5466 POM_5V_GPU 259/2177 POM_5V_CPU 1337/656
RAM 2392/3963MB (lfb 70x4MB) SWAP 140/8126MB (cached 3MB) IRAM 0/252kB(lfb 252kB) CPU [4%@1428,8%@1428,58%@1428,47%@1428] EMC_FREQ 4%@1600 GR3D_FREQ 4%@921 APE 25 PLL@40.5C CPU@45.5C PMIC@100C GPU@44C AO@52.5C thermal@45.25C POM_5V_IN 3288/5457 POM_5V_GPU 302/2169 POM_5V_CPU 1123/658
RAM 2615/3963MB (lfb 70x4MB) SWAP 140/8126MB (cached 3MB) IRAM 0/252kB(lfb 252kB) CPU [30%@1428,17%@1428,16%@1428,50%@1428] EMC_FREQ 5%@1600 GR3D_FREQ 6%@921 APE 25 PLL@40.5C CPU@46.5C PMIC@100C GPU@43.5C AO@52.5C thermal@44.5C POM_5V_IN 3331/5448 POM_5V_GPU 302/2161 POM_5V_CPU 1080/660
RAM 2776/3963MB (lfb 78x4MB) SWAP 140/8126MB (cached 3MB) IRAM 0/252kB(lfb 252kB) CPU [38%@1428,18%@1428,26%@1428,68%@1428] EMC_FREQ 6%@1600 GR3D_FREQ 4%@921 NVDEC 716 APE 25 PLL@40.5C CPU@46C PMIC@100C GPU@44C AO@52.5C thermal@45C POM_5V_IN 3968/5442 POM_5V_GPU 473/2154 POM_5V_CPU 1335/663
RAM 2839/3963MB (lfb 75x4MB) SWAP 140/8126MB (cached 3MB) IRAM 0/252kB(lfb 252kB) CPU [7%@1428,9%@1428,9%@1428,11%@1428] EMC_FREQ 16%@1600 GR3D_FREQ 99%@921 NVDEC 716 APE 25 PLL@41.5C CPU@47.5C PMIC@100C GPU@46C AO@53C thermal@45C POM_5V_IN 8350/5454 POM_5V_GPU 4463/2164 POM_5V_CPU 421/662
RAM 2824/3963MB (lfb 73x4MB) SWAP 154/8126MB (cached 3MB) IRAM 0/252kB(lfb 252kB) CPU [22%@1428,20%@1428,6%@1428,19%@1428] EMC_FREQ 24%@1600 GR3D_FREQ 99%@921 NVDEC 716 APE 25 PLL@41.5C CPU@48C PMIC@100C GPU@46C AO@53C thermal@46.75C POM_5V_IN 8308/5466 POM_5V_GPU 4259/2173 POM_5V_CPU 421/661
RAM 2824/3963MB (lfb 73x4MB) SWAP 154/8126MB (cached 3MB) IRAM 0/252kB(lfb 252kB) CPU [2%@1428,9%@1428,5%@1428,8%@1428] EMC_FREQ 30%@1600 GR3D_FREQ 99%@921 NVDEC 716 APE 25 PLL@41C CPU@48C PMIC@100C GPU@46.5C AO@53.5C thermal@47.25C POM_5V_IN 8266/5478 POM_5V_GPU 4217/2181 POM_5V_CPU 421/660
RAM 2825/3963MB (lfb 73x4MB) SWAP 154/8126MB (cached 3MB) IRAM 0/252kB(lfb 252kB) CPU [3%@1428,16%@1428,4%@1428,11%@1428] EMC_FREQ 34%@1600 GR3D_FREQ 99%@921 NVDEC 716 APE 25 PLL@41.5C CPU@48C PMIC@100C GPU@46C AO@53.5C thermal@47.25C POM_5V_IN 8152/5489 POM_5V_GPU 4175/2190 POM_5V_CPU 421/659
RAM 2825/3963MB (lfb 73x4MB) SWAP 154/8126MB (cached 3MB) IRAM 0/252kB(lfb 252kB) CPU [7%@1428,16%@1428,16%@1428,7%@1428] EMC_FREQ 36%@1600 GR3D_FREQ 99%@921 NVDEC 716 APE 25 PLL@41.5C CPU@48C PMIC@100C GPU@46.5C AO@54C thermal@47.25C POM_5V_IN 8152/5500 POM_5V_GPU 4048/2197 POM_5V_CPU 463/658
RAM 2825/3963MB (lfb 73x4MB) SWAP 154/8126MB (cached 3MB) IRAM 0/252kB(lfb 252kB) CPU [1%@1428,11%@1428,5%@1428,5%@1428] EMC_FREQ 38%@1600 GR3D_FREQ 99%@921 NVDEC 716 APE 25 PLL@41.5C CPU@48.5C PMIC@100C GPU@46.5C AO@54C thermal@47.5C POM_5V_IN 8110/5511 POM_5V_GPU 4012/2205 POM_5V_CPU 463/657
RAM 2828/3963MB (lfb 73x4MB) SWAP 154/8126MB (cached 3MB) IRAM 0/252kB(lfb 252kB) CPU [12%@1428,7%@1428,18%@1428,3%@1428] EMC_FREQ 39%@1600 GR3D_FREQ 99%@921 NVDEC 716 APE 25 PLL@41.5C CPU@48C PMIC@100C GPU@46.5C AO@53.5C thermal@47.5C POM_5V_IN 7953/5521 POM_5V_GPU 4012/2212 POM_5V_CPU 464/656

I hope gpu utilization reaches 99% but still we are getting 1fps.

hi,

i am trying to test deepstream with yolo-tiny, i have followed the steps in the readme, but whenever i run it i get a black window.

here is the terminal output:

Using winsys: x11 
Creating LL OSD context new
Deserialize yoloLayerV3 plugin: yolo_17
Deserialize yoloLayerV3 plugin: yolo_24

Runtime commands:
	h: Print this help
	q: Quit

	p: Pause
	r: Resume

NOTE: To expand a source in the 2D tiled display and view object details, left-click on the source.
      To go back to the tiled display, right-click anywhere on the window.

** INFO: <bus_callback:163>: Pipeline ready

Opening in BLOCKING MODE 
NvMMLiteOpen : Block : BlockType = 261 
NVMEDIA: Reading vendor.tegra.display-size : status: 6 
NvMMLiteBlockCreate : Block : BlockType = 261 

**PERF: FPS 0 (Avg)	
**PERF: 0.00 (0.00)	
**PERF: 0.00 (0.00)	
**PERF: 0.00 (0.00)	
**PERF: 0.00 (0.00)	
**PERF: 0.00 (0.00)	
**PERF: 0.00 (0.00)

please note that i am getting “** INFO: <bus_callback:163>: Pipeline ready”
but not “** INFO: <bus_callback:149>: Pipeline running” perhaps this is related to the issue?

any ideas?

Hi,

Have you set the DISPLAY parameter first?

# depends on your setting
export DISPLAY=:0
export DISPLAY=:1

Then you can execute YOLO with the command like this:

deepstream-app -c deepstream_app_config_yoloV2_tiny.txt

Thanks.

There has been a similar discussion here before, you can take a look at it and see if it helps - https://devtalk.nvidia.com/default/topic/1058668

Can you resubmit the example of YoloV3 with DS 4.0 and a video that really works? we are many people who We will have the same problem with this example.

Hi,

There are some pre-requirement to enable the YOLOv3 model with Deepstream 4.0.
We can see the YOLO result with the instruction shared in /opt/nvidia/deepstream/deepstream-4.0/sources/objectDetector_Yolo/README.

Could you follow the steps and try it again?
If you still meet issues, please share the error log or issue with us.

Thanks.

I did follow the README as possible. Please follow my procedure on top of this thread and point me where I did wrong

Thank you

  1. 1-2 fps you are seeing is the expected performance for yolov3 on Nano. You can switch inference mode to fp16 or you can try using yolov3-tiny for a higher throughput.

  2. Yes, yolov3 is a compute intensive model and it takes time to build it on a nano. Once it’s built, it will be saved in the same directory as the model file. You can update the “model-engine-file” config param in config_infer_primary_yolov3.txt file to use this engine file for subsequent runs and it wont be recreated.

Thank you sir.
This is exactly the answer I need

You can also use these sample config files to run yolov3 on nano at 20 FPS.

https://devtalk.nvidia.com/default/topic/1064871/deepstream-sdk/deepstream-gst-nvstreammux-change-width-and-height-doesn-t-affect-fps/post/5392823/#5392823

If the tracking results are bad for your test video, you can reduce the interval to improve the accuracy further but the FPS will drop as well. Its trade-off that needs to be tuned for your use-case.