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Convolutional Neural Networks for Pedestrian Detection

MATLAB 68.54% Shell 31.46%

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How to train models?

Is that like R-CNN?

MATLAB crash while loading model

I'm running MATLAB R2014a on Ubuntu 14.04 LTS.
I followed your instructions and tried to run your code.
I test rcnn code, i.e. run rcnn_demo, and everything works fine.
I also download your model and place in the right directory.
So when MATLAB trying to load model using following sentence:
rcnn_model = rcnn_load_model(rcnn_model_file, use_gpu);
MATLAB crashed.


------------------------------------------------------------------------
              abort() detected at Tue Oct 27 14:17:24 2015
------------------------------------------------------------------------

Configuration:
  Crash Decoding     : Disabled
  Current Visual     : 0x5e (class 4, depth 24)
  Default Encoding   : UTF-8
  GNU C Library      : 2.19 stable
  MATLAB Architecture: glnxa64
  MATLAB Root        : /usr/local/MATLAB/R2014A
  MATLAB Version     : 8.3.0.532 (R2014a)
  Operating System   : Linux 3.13.0-32-generic #57-Ubuntu SMP Tue Jul 15 03:51:08 UTC 2014 x86_64
  Processor ID       : x86 Family 6 Model 58 Stepping 9, GenuineIntel
  Virtual Machine    : Java 1.7.0_11-b21 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode
  Window System      : The X.Org Foundation (11501000), display :0.0

Fault Count: 1


Abnormal termination:
abort()

Register State (from fault):
  RAX = 0000000000000000  RBX = 00007f16c1eb5620
  RCX = ffffffffffffffff  RDX = 0000000000000006
  RSP = 00007f177f336458  RBP = 00007f177f336590
  RSI = 0000000000000c73  RDI = 0000000000000c42

   R8 = 000000000000ff08   R9 = ffffffffffff1150
  R10 = 0000000000000008  R11 = 0000000000000206
  R12 = 00007f177f336830  R13 = 00007f16d44f75e0
  R14 = 0000000000000001  R15 = 00007f177f3370b0

  RIP = 00007f17916c8cc9  EFL = 0000000000000206

   CS = 0033   FS = 0000   GS = 0000

Stack Trace (from fault):
[  0] 0x00007f17916c8cc9                    /lib/x86_64-linux-gnu/libc.so.6+00224457 gsignal+00000057
[  1] 0x00007f17916cc0d8                    /lib/x86_64-linux-gnu/libc.so.6+00237784 abort+00000328
[  2] 0x00007f16c1c8fd81             /usr/lib/x86_64-linux-gnu/libglog.so.0+00068993 _ZN6google22InstallFailureFunctionEPFvvE+00000000
[  3] 0x00007f16c1c8fdaa             /usr/lib/x86_64-linux-gnu/libglog.so.0+00069034 _ZN6google10LogMessage10SendToSinkEv+00000000
[  4] 0x00007f16c1c8fce4             /usr/lib/x86_64-linux-gnu/libglog.so.0+00068836 _ZN6google10LogMessage9SendToLogEv+00001224
[  5] 0x00007f16c1c8f6e6             /usr/lib/x86_64-linux-gnu/libglog.so.0+00067302 _ZN6google10LogMessage5FlushEv+00000414
[  6] 0x00007f16c1c92687             /usr/lib/x86_64-linux-gnu/libglog.so.0+00079495 _ZN6google15LogMessageFatalD1Ev+00000025
[  7] 0x00007f16d82f03bb /home/dst/Github/DeepPed_New/rcnn/external/caffe/matlab/caffe/caffe.mexa64+00394171
[  8] 0x00007f16d82b8b7a /home/dst/Github/DeepPed_New/rcnn/external/caffe/matlab/caffe/caffe.mexa64+00166778
[  9] 0x00007f16d82a6d9a /home/dst/Github/DeepPed_New/rcnn/external/caffe/matlab/caffe/caffe.mexa64+00093594
[ 10] 0x00007f16d82a7043 /home/dst/Github/DeepPed_New/rcnn/external/caffe/matlab/caffe/caffe.mexa64+00094275 mexFunction+00000203
[ 11] 0x00007f17895f372a     /usr/local/MATLAB/R2014A/bin/glnxa64/libmex.so+00120618 mexRunMexFile+00000090
[ 12] 0x00007f17895efa94     /usr/local/MATLAB/R2014A/bin/glnxa64/libmex.so+00105108
[ 13] 0x00007f17895f0fb4     /usr/local/MATLAB/R2014A/bin/glnxa64/libmex.so+00110516
[ 14] 0x00007f17889eaad9 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_dispatcher.so+00670425 _ZN8Mfh_file11dispatch_fhEiPP11mxArray_tagiS2_+00000697
[ 15] 0x00007f1787c872b4 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+04461236
[ 16] 0x00007f1787c88bc9 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+04467657
[ 17] 0x00007f1787c893fc /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+04469756
[ 18] 0x00007f1787b036e3 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02873059
[ 19] 0x00007f1787b1309e /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02936990
[ 20] 0x00007f1787b13183 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02937219
[ 21] 0x00007f1787c49172 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+04206962
[ 22] 0x00007f1787a7e589 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02327945
[ 23] 0x00007f1787a81167 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02339175
[ 24] 0x00007f1787a7f26f /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02331247
[ 25] 0x00007f1787a7fec4 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02334404
[ 26] 0x00007f1787add30b /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02716427
[ 27] 0x00007f17889eaad9 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_dispatcher.so+00670425 _ZN8Mfh_file11dispatch_fhEiPP11mxArray_tagiS2_+00000697
[ 28] 0x00007f1787ac120e /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02601486
[ 29] 0x00007f1787a7c1d0 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02318800
[ 30] 0x00007f1787a7e1ea /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02327018
[ 31] 0x00007f1787a81167 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02339175
[ 32] 0x00007f1787a7f26f /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02331247
[ 33] 0x00007f1787a7fec4 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02334404
[ 34] 0x00007f1787add30b /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02716427
[ 35] 0x00007f17889eaad9 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_dispatcher.so+00670425 _ZN8Mfh_file11dispatch_fhEiPP11mxArray_tagiS2_+00000697
[ 36] 0x00007f1787ac120e /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02601486
[ 37] 0x00007f1787a621b0 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02212272
[ 38] 0x00007f1787a7d25f /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02323039
[ 39] 0x00007f1787a81167 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02339175
[ 40] 0x00007f1787a7f26f /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02331247
[ 41] 0x00007f1787a7fec4 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02334404
[ 42] 0x00007f1787add30b /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02716427
[ 43] 0x00007f17889eac5f /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_dispatcher.so+00670815 _ZN8Mfh_file11dispatch_fhEiPP11mxArray_tagiS2_+00001087
[ 44] 0x00007f1787ab0135 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02531637
[ 45] 0x00007f1787a770d9 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02298073
[ 46] 0x00007f1787a73dc7 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02284999
[ 47] 0x00007f1787a74193 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwm_interpreter.so+02285971
[ 48] 0x00007f178981dafc /usr/local/MATLAB/R2014A/bin/glnxa64/libmwbridge.so+00142076
[ 49] 0x00007f178981e791 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwbridge.so+00145297 _Z8mnParserv+00000721
[ 50] 0x00007f1792ad492f   /usr/local/MATLAB/R2014A/bin/glnxa64/libmwmcr.so+00489775 _ZN11mcrInstance30mnParser_on_interpreter_threadEv+00000031
[ 51] 0x00007f1792ab5b6d   /usr/local/MATLAB/R2014A/bin/glnxa64/libmwmcr.so+00363373
[ 52] 0x00007f1792ab5be9   /usr/local/MATLAB/R2014A/bin/glnxa64/libmwmcr.so+00363497
[ 53] 0x00007f17871a9d46   /usr/local/MATLAB/R2014A/bin/glnxa64/libmwuix.so+00343366
[ 54] 0x00007f178718c382   /usr/local/MATLAB/R2014A/bin/glnxa64/libmwuix.so+00222082
[ 55] 0x00007f179322a50f /usr/local/MATLAB/R2014A/bin/glnxa64/libmwservices.so+02323727
[ 56] 0x00007f179322a67c /usr/local/MATLAB/R2014A/bin/glnxa64/libmwservices.so+02324092
[ 57] 0x00007f179322657f /usr/local/MATLAB/R2014A/bin/glnxa64/libmwservices.so+02307455
[ 58] 0x00007f179322b9b5 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwservices.so+02329013
[ 59] 0x00007f179322bde7 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwservices.so+02330087
[ 60] 0x00007f179322c4c0 /usr/local/MATLAB/R2014A/bin/glnxa64/libmwservices.so+02331840 _Z25svWS_ProcessPendingEventsiib+00000080
[ 61] 0x00007f1792ab6098   /usr/local/MATLAB/R2014A/bin/glnxa64/libmwmcr.so+00364696
[ 62] 0x00007f1792ab63bf   /usr/local/MATLAB/R2014A/bin/glnxa64/libmwmcr.so+00365503
[ 63] 0x00007f1792ab128f   /usr/local/MATLAB/R2014A/bin/glnxa64/libmwmcr.so+00344719
[ 64] 0x00007f1791a5f182              /lib/x86_64-linux-gnu/libpthread.so.0+00033154
[ 65] 0x00007f179178c47d                    /lib/x86_64-linux-gnu/libc.so.6+01025149 clone+00000109


This error was detected while a MEX-file was running. If the MEX-file
is not an official MathWorks function, please examine its source code
for errors. Please consult the External Interfaces Guide for information
on debugging MEX-files.

If this problem is reproducible, please submit a Service Request via:
    http://www.mathworks.com/support/contact_us/

A technical support engineer might contact you with further information.

Thank you for your help.

Need smaller SVM

I've been trying to run this but I ran out of GPU memory. Therefore I reduced the batch size and it works fine (i.e no cuda crash) however now I'm facing the problem that your SVM does not take batch reduction into account. Is there any workaround to this (or would you be so kind as to train the SVM again just with less features, I would be eternally grateful) or my only chance is really to get a better GPU?

EDIT: as a botched solution I just did:

    % load the trained SVM
    SVM = load('data/rcnn_models/DeepPed/SVM_finetuned_alexnet.mat');
    %PersonW = SVM.W; %Feature weights for scoring
    PersonW = SVM.W(1:end/2);
    PersonB = SVM.b; %Constant scoring factor

But I'm pretty sure this will be incorrect.

Thank you

`deepPed_demo` makes matlab crushed

I work fine with rcnn_demo but after I download all the codes and data from yours, running deepPed_demo makes matlab crushed saying "Matlab has encountered an internal problem and needs to close". My platform is Ubuntu 15.04 & Caffe v0.999 & Matlab2015b.

The errors are shown as follows:

[libprotobuf ERROR google/protobuf/text_format.cc:274] Error parsing text-format caffe.NetParameter: 7:7: Message type "caffe.NetParameter" has no field named "layer".
F0518 12:21:06.165700  6911 upgrade_proto.cpp:571] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse NetParameter file: model-defs/alexnet_deploy_fc7_CAFFE.prototxt
*** Check failure stack trace: ***

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