Multi object tracking is one of the most important topics of computer science that has many applications, such as surveillance system, navigation robot, sports analysis, autonomous driving car, and others. One of the main problems of multi-object tracking is occlusion. Occlusion is an object that is covered by other objects. Occlusion may cause the ID between objects to be switched. This study discusses occlusion on multi-object tracking and its completion with network flow. Given objects detection on each frame, the task of multi object tracking is to estimate the movement of objects and then connect the estimation objects corresponding to the objects in the next frame or well known as the data association. Notice that each object on a frame as a node, then there is an edge connecting each node on a frame with other frames, this architecture in graph theory is known as network flow. Then find the set of edges that provide the greatest probaility of transition from one frame to the next, or to the optimization problem well known as max-cost network flow. Edge contains information on how probabiltity a node moves to the node in the frame afterwards. This probability calculation is based on position distance and similarity feature between frames, the feature used is CNN feature. We modeled max-cost network flow as the maximum likelihood problem which was then solved with the Hungarian algorithm. The data used in this research is 2DMOT2015. Performance evaluation results show that the system built gives accuracy 20.1% with the ID switch is 3084 and fast computational process on 215.8 frame/second.
"Pengenalan emosi dasar melalui ekspresi wajah menjadi domain penelitian yang berkembang saat ini. Berbagai metode machine learning telah digunakan untuk permasalahan ini. Dewasa ini, metode deep learning terbukti lebih robust untuk penyelesaian domain pengenalan emosi dasar. Salah satu metode deep learning yang dapat digunakan adalah deep belief network-deep neural network (DBN). Metode ini sebelumnya berhasil diujikan untuk pengenalan citra CIFAR-10 dan MNIST, namun masih belum digunakan untuk dataset citra emosi wajah. Oleh karena itu, pada penelitian ini, kami menggunakan DBN-DNN untuk pengenalan emosi dasar. DBN-DNN diujikan dengan 2 (dua) skema eksperimen yakni DBN-DNN dimensi penuh dimensi tereduksi. Hasil dari kedua skema menunjukkan bahwa DBN-DNN berhasil diujikan pada dataset citra wajah MUG, CK+, dan IMED untuk pengenalan 7 (tujuh) kelas emosi dasar yaitu marah, jijik, takut, senang, netral, sedih, dan terkejut. Skema DBN- DNN dimensi penuh, berhasil mendapatkan akurasi pengenalan emosi dasar pada citra wajah dataset MUG sebesar 94.07%, dengan waktu komputasi yang cukup lama yakni 7 jam 13 menit. Berbeda halnya dengan pengenalan DBN- DNN dimensi penuh pada citra wajah dataset CK+ dan MUG, meskipun waktu yang dibutuhkan saat pengenalan cukup singkat yakni 11 menit untuk CK+ dan 7 menit untuk IMED, akurasi yang didapatkan masih cukup kecil yakni 40.64% untuk CK+ dan 44.43% untuk IMED. Kecilnya akurasi pengenalan CK+ dan IMED, dipengaruhi oleh jumlah data yang kurang banyak, berbeda dengan MUG yang mencapai 9805 data. Sehingga, DBN-DNN kurang optimal dalam melakukan proses pembelajaran pada kedua dataset tersebut, CK+ dan IMED. Sedangkan, pada skema DBN-DNN dimensi tereduksi, akurasi berhasil meningkat baik untuk pengenalan pada dataset MUG, CK+ dan IMED. Akurasi pengenalan pada MUG mencapai 94.75%, CK+ 52.84%, dan IMED 56.58%. Waktu komputasi yang diperlukan dalam pengenalan pun juga lebih efisien khususnya pada dataset MUG, menjadi 3 jam 45 menit termasuk proses reduksi dimensi SVD di dalamnya. Hal ini berbeda untuk dua dataset lain, CK+ dan IMED, keduanya membutuhkan waktu cukup lama untuk proses reduksi dimensi karena SVD menggunakan jumlah dimensi 16384 untuk mendekomposisi matriks. Namun, jika waktu yang digunakan untuk proses DBN-DNN nya saja relatif lebih singkat dari DBN-DNN dimensi penuh, yakni 2 menit untuk CK+ dan 1 menit untuk IMED.
Facial emotion recognition using facial expression has been popular in these past years. There are many machine learning methods used for recognition tasks. Currently, the most robust method for this domain is deep learning. One type of deep learning method that can be used is the deep belief network – deep neural network (DBN-DNN). Although DBN-DNN has been used for recognizing CIFAR-10 and MNIST datasets, it has not yet been used for facial emotion recognition. Hence, in this research, we attempt to use the DBN-DNN for recognizing facial emotions. This research consists of two experimental schemes, DBN-DNN with full dimension and DBN-DNN with the reduced dimension. The result of these experiments shows that using the MUG facial emotion dataset, DBN-DNN has successfully recognized 7 (seven) classes of basic emotions, angry, disgust, fear, happy, neutral, sadness, and surprise. DBN- DNN with full dimension has successfully reached 94.07% accuracy for recognizing 7 ( seven) basic emotions from the MUG dataset, even the run time needed is not efficient, 7 hours and 13 minutes. Meanwhile, the CK+ dan IMED dataset is not quite good at accuracy, even the run time is quite short, 11 minutes for CK+ dataset and 7 minutes for the IMED dataset. The accuracy for the CK+ dataset reaches 40,64% and 44.43% for the IMED dataset. This accuracy occurs because of the lack number of data that is processed by DBN-DNN. DBN-DNN is good at a lot of the number of data, like MUG with 9805 data. On the other hand, DBN-DNN with reduced dimension has successfully reached higher accuracy for MUG (94.75%), CK+ (52.84%) and IMED (56.58%) The run time also more efficient, especially on MUG Dataset (3 hours and 45 minutes). But, CK+ and IMED need a longer time for finishing the dimensionality reduction with SVD. Its because the number of dimensions processed by SVD uses a full dimension of the matrix, 16384. Hence, it needs more time to run the SVD. But, the time need for processing DBN-DNN after finishing the SVD, only need 2 minutes for CK+ dataset and 1 minute for IMED dataset.
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